Systems and methods for conducting/defending digital warfare or conflict

ABSTRACT

In exemplary embodiments, an AI (Artificial Intelligence) enabled system is developed, configured, and/or intended to facilitate the offensive, defensive, and logistical capabilities of a person engaged in warfare in some direct form. This encompasses scenarios ranging from the solo unit behind enemy lines, the operational deployment of forces and resources, to strategic theater. Exemplary embodiments include building or configuring a highly advanced and aware system, facilitated by AI, driven by cloud and edge computing, to allow decisions and tactical advantage be deployed and calculated at the level it needs first. Exemplary embodiments may be configured to address both the kinetic real world as well as digital cyberspace, incorporate offensive and defensive technology, and be primarily driven by the concept to augment forces to support and counter threats as well as malicious enemy AI.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit and priority of U.S. ProvisionalPatent Application No. 63/120,834 filed Dec. 3, 2020.

This application is a continuation-in-part of (1) U.S. patentapplication Ser. No. 16/700,601 filed Dec. 2, 2019 (published asUS2020/0107155 on Apr. 2, 2020); and (2) U.S. patent application Ser.No. 17/104,136 filed Nov. 25, 2020 (published as US2021/0084451 on Mar.18, 2021); and (3) U.S. patent application Ser. No. 17/192,381 filedMar. 4, 2021 (published as US2021/0202067 on Jul. 1, 2021).

U.S. patent application Ser. No. 16/700,601 is a continuation of U.S.patent application Ser. No. 15/840,775 filed Dec. 13, 2017 (now U.S.Pat. No. 10,477,342 issued Nov. 12, 2019), which, in turn, claims thebenefit and priority of U.S. Provisional Patent Application No.62/435,042 filed Dec. 15, 2016 and U.S. Provisional Patent ApplicationNo. 62/480,206 filed Mar. 31, 2017.

U.S. patent application Ser. No. 17/104,136 is a continuation-in-part ofU.S. patent application Ser. No. 16/654,708 filed Oct. 16, 2019 (nowU.S. Pat. No. 10,853,897 issued Dec. 1, 2020). U.S. patent applicationSer. No. 17/104,136 also claims the benefit and priority of U.S.Provisional Patent Application No. 63/011,949 filed Apr. 17, 2020.

U.S. patent application Ser. No. 17/192,381 is a continuation-in-part ofU.S. patent application Ser. No. 17/104,136. U.S. patent applicationSer. No. 17/192,381 also claims the benefit and priority of U.S.Provisional Patent Application No. 62/986,382 filed Mar. 6, 2020 andU.S. Provisional Patent Application No. 63/011,949 filed Apr. 17, 2020.

U.S. patent application Ser. No. 16/654,708 claims the benefit andpriority of U.S. Provisional Patent Application No. 62/746,330 filedOct. 16, 2018. U.S. patent application Ser. No. 16/654,708 is acontinuation-in-part of U.S. patent application Ser. No. 16/516,822filed Jul. 19, 2019 (now U.S. Pat. No. 10,497,242 issued Dec. 3, 2019).U.S. patent application Ser. No. 16/654,708 is also acontinuation-in-part of U.S. patent application Ser. No. 15/840,762filed Dec. 13, 2017 (now U.S. Pat. No. 10,477,342 issued Nov. 12, 2019).

U.S. patent application Ser. No. 16/516,822 claims the benefit andpriority of U.S. Provisional Patent Application No. 62/701,252 filedJul. 20, 2018. U.S. patent application Ser. No. 16/516,822 is acontinuation-in-part of U.S. patent application Ser. No. 15/840,762filed Dec. 13, 2017 (now U.S. Pat. No. 10,477,342 issued Nov. 12, 2019).

U.S. patent application Ser. No. 15/840,762 claims the benefit andpriority of U.S. Provisional Patent Application No. 62/435,042 filedDec. 15, 2016 and U.S. Provisional Patent Application No. 62/480,206filed Mar. 31, 2017.

The entire disclosures of the above applications are incorporated hereinby reference.

FIELD

The present disclosure generally relates to systems and methods forconducting/defending digital warfare or conflict.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

Offensive, defensive, and logistical capabilities may be needed for aperson engaged in warfare in some direct form. This encompassesscenarios ranging from the solo unit behind enemy lines, the operationaldeployment of forces and resources, to strategic theater.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations and are notintended to limit the scope of the present disclosure.

FIG. 1 is a diagram of an example system for determining location andcontext of an addict, a support network, and other information andaspects of an addict's personal and professional life for addictiontreatment purposes, including relapse prevention and containment. Thisdiagram includes various example networks and technologies that may beused for collecting and analyzing the addict's location and context.Also shown are example data sources and analytical engines that may beneeded to process such data and to identify and implement actions topreempt, prevent, and/or contain any relapse.

FIG. 2 describes an example Addict Monitor/Controller (AMC) device thatmay be used to collect, process, and disseminate context and addictiontrigger-related data from and about an addict via various sensors andother data collection mechanisms, and to interface with/to the addictand 3rd party mechanisms. The device may also provide mechanisms toprovide feedback to the addict and assist in the implementation ofrelapse-related preventative and containment actions.

FIG. 2a provides examples of distributed sensor deployment, datacollection options, localized sensors, and localized networks that maybe used in exemplary embodiments.

FIG. 2b provides examples of internet of things (IoT) addict-relatedsensors, devices, and networks that may be used in exemplaryembodiments.

FIG. 3 depicts example steps for monitoring an addict's triggers, and inthe course of doing so assessing/predicting the addict's risk ofrelapse. FIG. 3 also describes identifying possible resources that couldhelp the addict, and the actions that could be taken to prevent, preemptor contain a relapse. FIG. 3 also describes an example process forselecting such resources and actions.

FIG. 4 depicts an example system and example process for determining thelocation/context of an addict as well as the location/context of supportresources using a variety of sensors and other information sources.

FIG. 5 describes an example system and example process for assessing anaddict's trigger/relapse risk.

FIG. 5 also describes how such algorithms could be made self-learning tobetter assess an addict's relapse risk.

FIG. 5A depicts an example embodiment of a method for managing damagecontrol and recovering from a relapse situation.

FIG. 5B provides examples of risk, support areas maps, and map mashups.

FIG. 6 depicts example ways to identify/monitor trigger/relapse risk andidentifying, selecting, and implementing support resources and actions.

FIG. 6A depicts an exemplary embodiment of a trigger monitoring feedbackand learning system.

FIG. 7 describes example ways to identify/determine and select the bestactions and resources when relapse risk is high.

FIG. 7A describes an example of an action-determining subprocess—specifically, ways to utilize regularly scheduled addictcommunity meetings or spontaneous, unscheduled, flash addict communitymeetings.

FIG. 8 describes example ways to select the best interface(s) forinteracting with an addict, including implementing relapse preventionactions.

FIG. 9 describes an example addict rewards/demerits system based on anaddict's behaviors and actions, which may include rewarding (orpunishing) an addict based on behavior via tracking and data analyticsand various reward mechanisms.

FIG. 10 describes example ways in which addicts can receive and transmitsobriety ideas in public and private places via beacons. FIG. 10 alsoillustrates example ways in which Real-Time Location System (RTLS)technologies can be used to enable ad hoc, spontaneous, unscheduled, orflash addict meetings between people with similar addiction issues.

FIG. 11 thru 14 describe examples of using location and/or contextinformation to provide privacy and security for data collected invarious implementations of the present disclosure.

FIG. 15 depicts an example embodiment of a method for monitoring for arisk of a pre-identified behavior (e.g., pre-identified addict-relatedundesirable behavior, etc.). FIG. 15 also includes example triggers,priorities, and initial risk assessment/detection sensors.

FIG. 16 is a diagram of example conflict battlefield architecture.

FIG. 17 illustrates an example system according to an exemplaryembodiment focused on medicine and drug discovery for treatment ofdisease, including need-based driving, missing data collection, growingextensibility, skill instructional framework, human and AI elements,reputation management with the world, a moral compass/ethical frameworkto guide, and additional tasks.

FIG. 18 illustrates an example system according to an exemplaryembodiment including a moral compass.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

Addiction to substances (e.g., alcohol and chugs, etc.) and activities(e.g., gambling, etc.) are a major scourge of society. Addictions cancome in many forms, but generally can be put into two categories: 1)addiction to a substance, such as drugs, alcohol, or food, or 2)addiction to an activity, such as gambling, sex, or shopping. The humanimpact of an addiction can vary greatly in terms of physical toll on themind and body as well as everyday life-damage such as destruction offamilies and job loss. Common life-mining addictions include thoseinvolving alcohol, prescription and non-prescription drugs,cigarettes/nicotine, and gambling. Less common but very seriousaddictions involve overindulging in sex, eating, and avoidance/lack offood (e.g., anorexia or bulimia). Other addictions typically (but notalways) can be considered relatively minor or annoying such as shopping,exercise, work, sports viewing, beauty enhancement/plastic surgery,videogames, or even surfing the Internet or constant use of smartphones,to name a partial list.

The term addiction has many definitions, but in general it refers to aperson or persons who cannot, or will not, stop using or doing somethingthat is potentially harmful to them and/or others around them. Whileaddiction often conjures images of drunks or drug addicts roaming thestreets, in reality addiction impacts all walks of life, fromprofessionals to blue-collar workers to athletes to celebrities tostay-at-home parents, even to children. Many addicts live otherwiseuseful, functional lives, that can be greatly improved if theiraddiction is effectively treated.

Various types of addiction treatments, programs, and other methods foraddressing addiction have been around for many decades. Examplesinclude: 12-Step Programs such as Alcoholics Anonymous (AA),Acupuncture, Aversion Therapy (multiple forms), Behavioral Self-ControlTraining, Cognitive Therapy, Going Cold Turkey, Community Reinforcement,Diet-based Programs, Drug-based Treatments (multiple forms),Exercise-based programs, Hypnosis, Interventions, Meditation,Motivational programs, Nutrition-based programs, Rehabilitation(Inpatient and Outpatient)/Hospitalization stays, Religious-basedprograms, Self-Change Manuals/Guides, (Traditional) Psychotherapy(multiple forms), Spiritual Immersion, and Work/Treatment programs toname some of the more commonly-known approaches.

Sometimes addiction treatments such as those mentioned above work. Butvery often addiction treatments do not work, at least not for long.While there are many reasons why an addiction treatment might not work,one common reason is that most treatments are most effective when theyare actively being implemented, such as when a person is actively inresidence in a treatment facility, attending an AA meeting, or in atherapy session. Put another way, one of the vulnerabilities of theseand many of the above approaches is that their effectiveness is closelytied to their immediacy, both in terms of physicality to the addict (fortreatments that have a personal counseling and/or physical locationelement) and in time—how fresh the teachings of the program are in themind of the addict, not to mention how long the addict is willing toactively participate in treatment or treatment-related activities. Thisis particularly true for many of the most common treatments, such asrehab facility stays (inpatient or outpatient), therapy sessions, oralcoholism-related community meetings. Once the addict leaves those(usually physical, but increasingly virtual or augmented) places wherethe treatment takes place, the lessons or motivations from thosetreatments become weaker or start to fade, while at the same timeopportunities and temptations to partake in the addiction increase.

Another issue with the above treatments is that many surprisingly placelittle emphasis on the triggers that may cause or set off one'saddiction—particularly in understanding what situations, circumstances,or mindsets (broadly, triggers) make or drive the addict to want to usethe substance and/or activity in question. Triggers are what very oftendrives the desire to use. By way of background, triggers may besituations, circumstances, activities, events, and mental thoughtprocesses and frames-of-mind that tempt or cause an addict to want touse a substance or engage in an activity. Without such detailedknowledge of the triggers of addiction, such treatments often focus onsymptoms, or controlling such triggers after they occur. In any event,even in treatment programs where understanding of addiction triggers isemphasized, such emphasis is usually focused on general understanding ofthe triggers, not in dealing with them on a practical, day-to-day,hour-by-hour basis of everyday life.

As the addict gets farther away from the treatment program—physicallyand/or in time—the lessons from those programs naturally start to fade,becoming less effective, and, in turn, making the addict moresusceptible to relapse. Thus, it is desired to find ways of keepingtreatment lessons fresh in the mind of the addict, or alternativelyrefreshing them in a way to actively deter an addict from relapsing. Itis also unfortunately true that even the best-intentioned addict cannotsee all the possible situations that might tempt or trigger the addictto relapse in time to avoid such triggers, or at least mitigate them—theaddict may find himself in a high-risk environment before realizing it.Accordingly, there is a need for a way to use technology to providetreatment reinforcement when the addict is away from the place ofprimary treatment(s), while simultaneously protecting the addict fromthe temptations/addiction triggers of their addiction.

As mentioned earlier, some, but not all of the treatment programs aboveinclude education about addiction triggers. Such triggers includesituations, circumstances, activities, events, and mental thoughtprocesses and frame-of-minds that tempt or cause an addict to want touse a substance or engage in an activity known or identified to bedetrimental to the addict and/or others. Examples of triggers includebut are not limited to: Anger, Anxiety, Boredom, Change, Children,Conflict, Depression, Disorder, Embarrassment, Escape, Envy, Excitement,Fun, Frustration, Guilt, Health issues, Holidays Hunger, Insomnia, Jobstress, Loneliness, Mid-life Crises, Money worries, Noise,Overconfidence, Pain, Peer Pressure, feeling Powerful or Powerless,Proximity (to the substance), Fear of Quitting (the substance oractivity), Relationship issues, Relatives, Reminders, Sex, ShoppingSituations, Social Situations, Special Occasions, Stress, Taste andSmell, Times of Day, being Tired, being Unfun, being a Victim (or crime,abuse), ex-spouses/partners, Yelling, even Season or Weather changes orMusic. There are potentially hundreds of possible addiction triggers,and many thousands of trigger combinations.

It is when an addict's most vulnerable triggers become active or arepresent that the addict is most vulnerable to relapse. Thus, if boredomis a major trigger for an addict, it is imperative to keep the addictfrom becoming bored, or failing that, to respond to, and correct a boredaddict before the temptation to use a substance or engage in anaddictive activity becomes too strong and relapse occurs. Determining anaddict's triggers and proscribing actions and activities to deal withthose triggers without relapsing is anticipated to become a more commonand important part of many addiction treatments and is at the core ofvarious exemplary embodiments of the present disclosure. In particular,various exemplary embodiments disclosed herein focus significantattention on understanding an addict's triggers and, in particular,using an array of sensors and other information to anticipate and/ordetect triggers that are active, present, or in danger of becomingactive or present, particularly by using location and context to a)anticipate, predict, and/or preempt an addict's triggers from becomingactive or present, b) prevent a relapse when triggers do become activeby initiating one or more actions, activities, and/or contacts with anaddict's support network, and/or c) contain or manage a relapse if andwhen its occurs.

Surprisingly, there is relatively little application of technology inaddiction treatment in the prior art, less that incorporates location,and practically nothing that utilizes context. For example, locationtechnology has been used to aid patient recovery, but this was focusedon physical rehabilitation of ambulatory (hospital) patients, notaddiction recovery nor patient monitoring outside a hospitalenvironment. Location technology has also been used as a small part ofan Internet addiction treatment, which focuses primarily onunderstanding the amount and type of internet activity that is takingplace (e.g., games, certain types of websites, etc.). This latterexample also utilized the concept of support groups, but not in a mannerthat emphases location or context of the addict or the support group.

Exemplary embodiments of addiction treatment systems and methods aredisclosed herein. One example embodiment of a system includes aplurality of user devices, sensors, and other technology to: determine,through one or more communications networks, the location of an addictand the context of the addict at the location; evaluate a risk ofrelapse by the addict in relation to the location and/or the context;facilitate one or more actions and/or activities to mitigate the risk,if any, and/or react to a relapse, if any, by the addict. By way ofexample, the context may include a situation, environment, and/or stateof mind of the addict at the location. The context may include why theaddict is at the location, who the addict is with at the location, whatthe addict is doing at the location, when (day/time) the addict is atthe location, and/or how the addict got to the location, etc.

One example embodiment of a method of treating an addict for anaddiction includes determining, through one or more communicationsnetworks, the location of an addict and the context of the addict at thelocation, where the context includes a situation, environment, and/orstate of mind of the addict; evaluating a risk of relapse by the addictin relation to the location and/or the context; and facilitating one ormore actions and/or activities to mitigate the risk, if any, and/orreact to a relapse, if any, by the addict. This may be done primarily bydetermining the location and context of the addict and assessing thatdata relative to the addict's addiction triggers.

In various exemplary embodiments, a plurality of user devices, sensors,and/or other technologies are provided to protect privacy and securityof information collected as disclosed herein. In some exemplaryembodiments, experience-based data, including but not limited tolocation and/or context data, may be used to condition access toprotected information. Access to the protected information may bepermitted to a permittee based on recognition by the permittee of theexperience-based data.

The distinction between location and context and its importance shouldbe noted. The location of a person is often (not always) part ofdetermining a person's broader context. With respect to exemplaryembodiments of the present disclosure, depending on the circumstances ofan individual, the location of the person may be most important; inother circumstances, the broader context may be more important. Forexample, if the methods and/or apparatus described in the presentdisclosure determine that the best course of action may be to attend anearby AA (Alcoholics Anonymous) meeting, the specific circumstances ofthat person at that time may dictate that the addict should attend thenearby AA meeting. In another exemplary embodiment, themethods/apparatus of the present disclosure may determine that for theaddict's current situation and addiction inducing trigger level (e.g.,anxiety trigger, etc.), the addict should meet a certain person in theaddict's support network who also has the same or similar issues (e.g.,anxiety and alcoholism etc.) issues at an (e.g., anxiety-related, etc.)meeting that is quite a bit farther away than the addict's currentlocation. Here, the context of the addict is more important than justthe addict's location.

In various embodiments of the present disclosure, addicts can be helpedto detect and deal with the triggers that initiate or enhance thecraving to indulge in their addiction. There generally are many suchtriggers, including but not limited to: Anger, Anxiety, Boredom, Change,Conflict, Depression, Disorder, Envy (desire to) Escape, Excitement,Extreme Emotions, Fear, Frustration, Guilt, Health problems, Holidays,Hunger, Insomnia, Job issues, Kids/Children, Loneliness, Media (TV,Radio, the Internet) marketing, Mid-Life Crisis, Money problems, Music,Noise, Overconfidence, Peer Pressure, Power, Powerlessness, Proximity(to an addictive substance), (fear of) Quitting, Relationships,Relatives, Reminders, Sex, (change of) Seasons, Smell, SocialSituations, Stress, Taste, Times of Day, (being) Tired, (feeling) NotFun or Unhappy, (feeling) Victimized, Weather, Yelling, and Zeal (highenergy). Many addicts are especially vulnerable to relapsing when facedwith one or more of these triggers. Some of these triggers have alocation dimension to them, most notably proximity to an addictionsubstance or activity, and many more have a location element in theactions or solutions for dealing with those triggers (and/or high-riskfor relapse situations) without relapse. For example, a response to thedetection of the Boredom trigger may require the addict to go to acertain place to do a certain activity. Loneliness would involvevisiting with or visit by a member of the addict's support network.Noise could require going to a quiet place to meditate, such as a churchor library, or to retreat to a serene program on a virtual realitydevice. This kind of information could be captured in a data profile(e.g., an addict profile including actions to take in relation to theaddict, etc.) stored in a database or similar data store.

The present disclosure includes various exemplary embodiments of systemsand methods that utilize the location and context of an addict and otherresources to a) preempt trigger and/or high-risk relapse situations, b)prevent relapse in high-risk situations, and/or c) respond to, manage,and recovery from a relapse when they do occur. Various embodimentsinclude collecting, aggregating, and analyzing addict- andaddiction-related data specific to that addict's condition,vulnerabilities, motivations, and usage triggers. Such data/informationcan be collected from a wide variety of sensors and other data sources,including but not limited to: personal devices such as smartphones,tablets, computers, PDAs, wearables (data collection devices worn on theperson, such as Fitbit, etc.), implants, Google Glass, etc.; nearbysensors or devices such as security/video cameras, smart devices (suchas smart home-related sensors, etc.), crowdsourcing data collectionapplications of nearby users, building/store/office Wi-Fi networks,location-sensitive beacons, etc.; and/or extended data collectionmechanisms such as road traffic sensors, public video cameras orbillboard displays, weather data collection sensors, lawenforcement/security-related devices, etc.

Various system and method embodiments according to the presentdisclosure make use of trigger-based sensor networks and trigger-basedsupport networks that may be tuned or modified so as to collect datapotentially related to one or more particular addiction triggers, suchas Anger, Frustration, Noise, Social Situations, Stress, Yelling, etc.Such data solely or in combination can identify various high risk (ofaddiction usage) contexts or relapse situations, circumstances, events,and/or possible mental frame-of-mind/thought processes that often haveto be managed to allow the addict the ability to successfully deal withsuch situations without succumbing to the addiction(s). This managing ofsuch situations may include providing, recommending, and/or injectingactions, activities, resources, recommendations, directions, and/orelements of control into the addict's life on either an ad-hoc,occasional, periodic, and/or (near) continuous manner to help the addictto refrain from their addiction. Management of such situations can bedone via a variety of analysis, assessment, and prediction engines andalgorithms that anticipate or predict the impact of certain situations,contexts, circumstances, or events on an addict's behavior and overallsobriety and devise and quickly put in place a course of action tominimize the addict's risk of relapse, or failing that, minimizing anyresultant harm and damage. Such a course of action may be predominatelylocation-based, meaning using location information as a key part of thecourse of action, but the present disclosure is not limited tolocation-based information; key information may well includenon-location based elements, particularly the use of sensors that canprovide valuable input into understanding the current context of theaddict, and actions that may have little or nothing to do with anaddict's location (such as the addict calling a family member to discusshis Frustration, for example).

While various aspects of exemplary embodiments of the present disclosureare targeted at the treatment of addiction, actual addiction is notalways involved. As noted, before, exemplary embodiments of the presentdisclosure can be used for the prevention of addiction, dealing withpossible or actual use or misuse of substances and/or activities thatcan potential be harmful to individuals or groups, or indeed unrelatedto any addiction or substance/activity use/misuse at all. In order toutilize and receive the benefits of exemplary embodiments of the presentdisclosure, a person does not necessarily have to be an addict with anaddiction to a substance and/or activity. For example, a person may wantto cut back on drinking, for example, even if not physically addictedthereto or even if the person does not drink often. Treatment may referto any assistance provided in various exemplary embodiments to help anaddict and/or others in dealing with the addiction (as described broadlyherein) in some form or fashion, and/or for informational purposes.Sober may refer to non-usage of the addiction substance/activity.Relapse may refer to usage of the addiction substance/activity.

Exemplary embodiments of the present disclosure may also be applicableto persons and/or situations in a pre-addiction situation or scenariowhere the substances/activities are merely abused, that is, done more ormore often than might be considered acceptable, healthy, or desirable.Thus, various embodiments of the present disclosure may be applicable inrelation to persons who may be considered high-risk, such as thechildren of alcoholics, though no symptoms of addiction exist. Variousembodiments of the present disclosure further may be applicable inrelation to situations and/or scenarios where person(s) are neitheraddicted nor considered abuser(s) nor high-risk; rather, they and/orothers would like to reduce the usage of a substance or activity toachieve some real or perceived benefit. The above situations/scenariosand other applicable contexts may be generally included under the termaddiction, and a person suffering from addiction, abuse, or the generaldesire to reduce/stop doing some substance and/or activity may also bereferred to as an addict. Also, an addiction or addict is not limited tojust one substance or activity; many addicts concurrently or seriallysuffer from more than one addiction (sometimes referred to as dualdiagnosis, though it can actually be 2 or more addictions). Variousembodiments of the present disclosure can be equally applicable to suchpersons with multiple addictions, either concurrent or consecutive.

In addition, a distinct set of exemplary embodiments related to prisonerand particularly parolee tracking and monitoring is enabled by thepresent disclosure. Such tracking and monitoring are currently done by aGPS bracelet attached to the person's ankle or other body part, withtheir movements then monitored via GPS readings. In situations where GPSdoes not work or work well, particularly in a building or otherstructure or environment where GPS does not work, cell towertriangulation is employed to provide a rough calculation. Both havelimitations: GPS with its primary use cases of being tracked outdoors,and the inaccuracies associated with cell tower triangulation for indoorsituations.

Because of these limitations, GPS bracelets in effect determine the typeof prisoner/parolee tracking that can be done, limiting the person to aparticular area or building. It cannot get more “micro” than that and/orcannot granularly track the person's location and activities in anindoor environment, which is, however, possible with exemplaryembodiments of the present disclosure. For example, a parolee could beconfined to house arrest in a multi-unit apartment or motel due to theenhanced ability to track a person's movements indoors. In addition,activities can be monitored or controlled, such as a person on parolefor a DUI (Driving Under the Influence) being prohibited from drinking.The sensors associated with the present disclosure as well as riskcalculation algorithms could be used to detect high risk situationswhere the parolee is about to violate parole, generating various alarms.If the parolee followed through and drank, then exemplary embodiments ofthe present disclosure would provide the evidence needed to revokeparole.

Context may generally refer to an addict's situation, environment,and/or state-of-mind (e.g., as determined by biometric data, etc.)particularly as it relates to a potential substance abuse relapse.Traditionally in mobile systems, a person's physical location can form akey cornerstone of that person's context—and indeed may be all that isneeded to determine the person's overall context in many instances. Forexample, if an alcoholic has stopped at a bar on his way home from work,it typically takes no additional data to infer a high-risk relapsesituation and state-of-mind. However, other or additional sensors may beused to confirm and/or refine a person's context. For instance, a lightsensor on an addict's device may indicate that the person is stilloutdoors (perhaps debating himself in the parking lot). A breathalyzersensor could indicate a relapse—that the situation has moved fromneeding a prevention set of actions to a set of damage control andsafety actions. Thus, an addict's location may be considered theentirety of the context (stopped at a bar), part of the context (not yetin the bar), or perhaps even not a key part of the context (e.g., theperson is drinking or is drunk, having left the bar, etc.). However, formany if not most embodiments, physical location plays at least a partialrole in determining a person's overall context; in addition, thelocation of support resources very often plays a key role in if and/orhow such resources may be employed.

In various exemplary embodiments of the present disclosure, systems andmethods are provided for preempting, anticipating, and/or detecting highrisk addiction relapse situations and determining and implementingactions and activities to prevent a relapse from occurring, or in thecase of actual relapse minimizing the associated damage and returningthe addict to sobriety as soon as possible. Various mechanisms areprovided for determining and utilizing an addict's context—particularlylocation—in assessing their risk of relapse and utilize that context aswell as the relative locations and contexts of other resources todetermine and implement relapse preventative actions. In variousexemplary embodiments of the present disclosure, location/context-basedmechanisms are provided to minimize or contain the consequences in theevent that a relapse occurs, as well as mechanisms to preempt andprevent high risk situations from occurring

At a more granular level, an example communications network includes aplurality of heterogeneous, differing, or different types of sensingdevices configured to monitor the location and/or context of an addict;and a plurality of heterogeneous, differing, or different types ofinterface devices each configured to engage in interaction with theaddict, with a support person for the addict, and/or with a third partyin the event that the network detects a relationship between themonitored location and/or context and a trigger predetermined in thenetwork for the addict as being related to relapse; wherein theinteraction is selected based on the trigger and the monitored locationand/or context.

The example communications network may include one or more server,client, cloud, peer-to-peer, and/or other devices configured to developand/or update a profile of the addict based on monitoring data from thesensing devices and/or the interaction engaged in by one or more of theinterface devices.

In the above example communications network, one or more of the sensingdevices may be configured to detect and/or determine the relationshipbetween the trigger and the monitored location and/or context.

In the above example communications network, the sensing devices may belocated in, on, and/or near the addict, and/or elsewhere relevant to acurrent and/or future location/context of the addict.

In various aspects of the present disclosure, a network-implementedmethod of providing support for an addict includes monitoring thelocation and/or context of the addict, the monitoring performed by oneor more sensing devices; detecting a relationship between the monitoredlocation and/or context and a trigger predetermined in the network forthe addict as being related to relapse; and based on the detectedrelationship, one or more of a plurality of interface devices of thenetwork interacting with the addict, with a support person for theaddict, and/or with a third party.

The foregoing example method may include, based on the monitoring, thedetecting, the determining, and/or the interacting, developing and/orupdating a profile of the addict, the profile including actions to takein relation to the addict.

In the foregoing example method, one of the sensing devices may beconfigured to send the monitored location and/or context and/or thedetected relationship to one or more other devices of the network.

In the foregoing example method, the sensing devices may be one or moreof the following: located in, on, and/or in the vicinity of the addict,mobile, and stationary.

FIG. 1 is a high-level summary diagram that shows an addict withassociated devices, sensors, wearable/embedded tags, and other locatabletechnology. FIG. 1 also shows an exemplary scope of potential people,resources, assets, locations, applications, and data that may be helpfuland/or important in helping the addict become and stay sober. Associateddevices generally refer to any technology that may be directly orindirectly associated with the addict for collecting data on or aboutthe addict and/or for disseminating data or actions to or about theaddict. Thus, an addict does not have to be in physical contact with adevice for a given exemplary embodiment to work. For example, anassociated device could be the use of a drone to shadow an addict'smovements, location, and behavior and reporting that information back toanalytical engine(s) of the given embodiment. The scope of potentialresources is not limited to those listed in FIG. 1 as other resourcesmay be used in other exemplary embodiments.

FIG. 1 illustrates various example embodiments 100 of the presentdisclosure including the use of an addict's support network 102. Asupport network is generally considered people who have some knowledgeof and/or influence about the addict's problems and are prepared tohelp. The support network 102 of people may include medicalprofessionals, friends, family members, therapists, co-workers,spouses/partners, social workers, advocates, pastors, priests,rehab/treatment centers, emergency responders, lawyers, courts, paroleofficers, social/business networks, family support networks, specializedsocial media (Addiction, Trigger-Focused) support, other websites,internet/cloud help, addiction community members, other addicts (using,recovering) or anyone who might be aware of the addict's situation andin a position (including physical location) to assist the addict in someform if the need arises (e.g., a support person, etc.).

Various example embodiments of the present disclosure provide forcontinuous, periodic, ad-hoc, and/or as-needed monitoring 103 d of thesupport person's location and/or status for potentially helping one ormore addict's, such as but not limited to Busy, Work, Available, InEmergency, Please Find Someone For Me To Help (e.g., assist anyone, notjust the persons listed as approved assisters), and/or schedule/calendarfor one or more of those support persons and associated respectivestatuses. This monitoring could be initiated by the Addiction Server, bythe support person's device(s) 102 a, 102 b, 102 c, 102 d, 102 e, cloudbased services, and/or 3rd party applications that already make use oflocation and/or status monitoring.

While FIG. 1 shows a variety of networking and communicationstechnologies, systems, and architectures, such as wirelesscommunications, client-server, peer-to-peer, and cloud computing, thepresent disclosure is not limited to these. For example, an architecturemay be deployed that deploys disclosed functionality for a very limitedarea for a select type of receiver/person for a limited period of time,such as spontaneous, unscheduled, or flash (short notice) drinkingtrigger meeting (a kind of specialized Alcoholics Anonymous meeting).Some or all of these attendees might have a specially issued RFID-type(printable, downloadable, or temporarily/specialty activated) tag,and/or beacon-based network that allows them access to the meeting andinterfaces with other personal technology that enables them to enjoybenefits from the meeting, such as personalized holographicpresentations to their personal visor, specialized drug doses, or justvalidation of their identity. Addiction FOB or dongle-type devices mightalso be used that serve as the interface means between such meetingtechnology and other personal technology such as a wearable orimplantable, or the Addiction Monitor/Controller device 200 shown inFIG. 2.

FIG. 1 also discloses an example Addiction Server/Cloud/Internet ofThings/Client Application(s) and Processing Networks 103 (server 103 a)that can be a key hub for communications with a variety of people 102,resources, assets, applications, and data sources that may haverelevance to the addict. As shown, the data sources may include adatabase 103 b of support network data (e.g., location,availability/schedule, specialties, privacy requirements or regulations,etc.) and a database 103 c of third-party app data and interfaces (e.g.,social medial, local search, navigation, etc.) and affinity programs.The data sources may also include data sources accessible over a network118 (e.g., local network, public network, private network, internet,IOT, etc.) such as a database 127 of addict data (e.g., medical,professional, public records, media, etc.), a database 128 of localaddiction data (e.g., police reports, trends, etc.) and a database 129of local data feeds (e.g., events, traffic, news, weather, camera feeds,etc.). Additional data sources may include addict data sources includingaddict data and analytics 104, including predictive analytics data, etc.The addict data and analytics 104 may include privacy, security,rewards, motivational database(s) and engine(s) 105, action/responseengine, interface coordination database(s) and engine(s) 106,risk/relapse assessment/prediction, learning database(s) and engine(s)107, (trending) context and behavior inference database(s) and engine(s)108, addict profile, support network, schedule/calendar,devices/vehicles 109, addict usage triggers, hobbies, media posts,behavioral data 110, location/context profiles, historicallocation/context data 111, high risk locations, places of interests(POIs) suppliers, enablers 112, addict medical, personal data 113, andadministration, security, and verification 114.

The server 103 a also serves as the primary analytical engine fordeveloping and processing algorithms for profiling an addict's behavior,tendencies, risks, and probabilities of relapse for a wide range ofpossible situations, and for determining a variety of actions to, for,or on behalf of the addict to avoid relapse and/or improve the addict'soverall treatment. Included in potential actions are monitoring thelocation of the addict and the addict's support network for scenarioswhere one or more support persons may be dispatched to the addict'slocation, or vice versa.

Such server functionality can be physically and/or logically configuredin many forms. It can be centralized in one or more servers. It can bepartitioned in a centralized manner such that functionality is splitamong different servers, such as one server being a communicationsnetwork front-end for communicating with various addicts, devices,sensors, and other networks, while another server or set of servers doesthe analysis of the data. It can also be architected in distributedmanner such that some or all of the functionality is performed on addictand/or support network devices. It can be architected such that some orall of the functionality is done in the Cloud via various forms of cloudcomputing. Regardless of physical and/or logical distribution offunctionality, it may be described as or referred to as a server unlessotherwise indicated.

The server serves as a monitoring, assessing, and controlling functionof, for, and/or on behalf of the addict. This could include providing avariety of alerts to various resources that the addict is in a high-risksituation or area. This control could further extend to actions such asdisabling the addict's car, informing the addict's addiction sponsor orcommunity members, or alerting family or law enforcement about dangeroussituations. Indeed, one example embodiment of the present disclosureprovides a form of involuntary monitoring and action coordination, notunlike GPS ankle bracelet monitoring, where an addict on parole foraddiction-related offenses (e.g., DUIs, etc.) may have their devices(and even attached sensors such as Blood Alcohol Content sensors)monitored to detect the presence of offending substances andimplementing actions to mitigate the risk to the community and theaddict themselves.

Another aspect of exemplary embodiments of the present disclosure is theuse of multiple location determination technologies or sources 101 todetermine locations of addicts and other persons/places/things. Thesetechnologies or sources 101 include, but are not limited to, sensornetworks (e.g., Internet of Things (IOT) 101 a, etc.), GPS/Assisted GPS101 b, cell tower identification 101 g, cell tower triangulation (TDOA,AFLT), beacons 101 c, Radio Frequency fingerprinting, Real-Time LocationServices (RTLS) 101 e, WiFi based location systems 101 f, RadioFrequency Identification (RFID) based location systems and similarsystems, drones 101 d, crowdsourcing, hybrids 101 i, simultaneouslocalization and mapping (SLAM) 101 h, and/or combinations of these orother location determination systems. These location determinationsystems may be on, worn or carried by, used by, embedded in, or nearbythe addict or addiction-related resource sufficiently to determineapproximate location.

Not all aspects of the present disclosure need to be centralized in theaddiction server. The addict's local device(s) 115 a may also havefunctionality as disclosed herein, both for Peer-to-Peer, IoT, Mesh,ZigBee, LPWAN, Star, Client/Server, and/or machine-to-machine (M2M)networking, situations and in circumstances where the addiction serveror other parts of the present disclosure are not operating oraccessible. An example of this functionality is in the deviceon/in/around the addict detecting a high-risk situation and the addictattempting to enter and drive a car in an underground garage (therebypreventing a GPS locate). The addict's device would automaticallyconnect with the vehicle's transportation system 119 (e.g., personalvehicle, friend or colleague's vehicle, transportation service likeUber, airlines, public transportions, etc.) to inform or provide analert of a high-risk situation and proceeding to disable the car.Indeed, many, even all of the server's functions could conceivably bedone in one or more of the addict's device(s) or in other computing/dataprocessing architectures such as cloud computing; a centralized serveris a convenient/logical way to represent many of the presentdisclosure's functions, but not inherently necessary to its overallfunctionality. For example, the risk/prediction engine part of theserver could easily be resident on the addict's device(s)(client).Indeed, in one exemplary embodiment, many or even all functions could beresident and/or controlled on the client.

Similarly, not all devices that can be used are depicted in FIG. 1.Devices 115 a that can be associated with the addict include but are notlimited to portable devices such as mobile phones/smartphones, tablets,laptops, other portable or mobile devices, etc.; wearable devices andtags on or in clothing, jewelry, shoes, watches, etc.; mobile paymentdevices/wallets, etc.; embedded sensors, tags, chips or otherelectronics that can be implanted or ingested (e.g., ingestibles orimplantables, etc.) in an addict, augmented reality andheads-up-displays (e.g., Google Glass, etc.) and virtualreality-enabling systems. Fixed or mobile/fixed hybrid devices 115 bsuch as desktop computers and smart home connected devices that can alsobe associated with the identity and/or location addict are also part ofaspects of some exemplary embodiments of the present disclosure. Forexample, FIG. 1 shows additional examples of smart home connecteddevices 115 b including a TV, refrigerator, and microwave. As more andmore devices become smart, the smart device will have the ability tocapture data that will help determine a person's location/contextthrough onboard or connected data capture devices such as video, audio,and/or other sensors. Combined with the device's known location (orability to determine the device's location), and the connectivityassociated with communicating to and from these devices (also known asthe Internet of Things or IoT), these devices/networks may provide newkey sources of personal context information.

FIG. 1 also discloses a variety of location and categories useful in awide variety of embodiments, not limited to the addict, supportresources, or even enforcement resources. One key concept can be toutilize these varieties of location types and categories in a variety ofways in supporting the above resources. These include, but are notlimited to:

-   -   Base Locations 121 including common locations for the addict,        such as home, work, school, or church, etc.;    -   Frequent Locations 122 including locations frequented often by        the addict, such as homes of friends or family, stores,        restaurants, malls, gym, hobbies, etc.;    -   High Risk Areas, Events, Locations 123, such as liquor stores,        casinos, concerts, drug-dealing areas that could pose a        temptation for the addict, bad peers, trigger activating        situations, etc.    -   Sanctuary Areas, Events, Locations 124 where an addict might        feel safe and have a very low temptation to use, such as AA        meetings, churches, key/safe friends and family members, dry        public areas or events;    -   Virtual Locations 125 including online forums, such as Facebook,        Twitter, Women for Sobriety, therapy sites/sessions, Virtual        Reality Locations, addiction communities where an addict can        involve him or herself safely (without using) in an online        activity. This includes the use of virtual and/or augmented        reality to put oneself in a different (safer) location, context,        and/or frame-of-mind.

While location is most often a key distinguishing characteristic,context can also be important, particularly for support network members.Just having a support person nearby in time of trouble is not enough,the person needs to be available, interested, and in a position (e.g.,situation/context) that he/she can break away from whatever they aredoing to help the addict. Thus context-determining sensors and othermechanisms can be important not only to the addict but the supportnetwork as well.

Put another way, Base Locations are where the addict is frequently, suchas Home, Work, or School. Frequent Locations are where the addictfrequently visits such as family and friends, the addict's gym,frequently visited stores or restaurants, and various hobby locationssuch as a bowling alley. High-Risk Locations are another element used invarious embodiments, to track possible high-risk areas for the addictwith both fixed locations (such as liquor stores, casinos) and varyinglocations (such as recent drug-activity areas). Opposite these high-risklocations would be Sanctuary Locations, where the addict will presumablybe especially safe from high-risk situations, such as an AA meeting orchurch service. The use of Virtual Locations is also disclosed, usedwhen an addict is logged into and/or viewing a service like Facebook orTwitter or is using Virtual Reality devices such that the addict is onthose applications and can be contacted or otherwise influenced by thoseapplications.

These location categories have a wide range of uses. They can be anintegral part of action determination when relapse risk is high, byfinding the closest Sanctuary Location, for example, and arranging for anearby support person to meet the addict there, including providing eachof them directions via their navigation application based on theircurrent location. High-risk locations of course are to be avoided, butthis can be done in many ways. They can be omitted from navigationapplications (de-augmented) to prevent temptations. Geo-fences can besetup around them such that when the addict enters one, automatic alertsare sent to nearby support persons to give them a heads up to possiblyprepare for an interception. Indeed, geo-fences can play an importantrole in various embodiments, e.g., in risk prediction (e.g., if ahigh-risk location geo-fence is violated then risk score goes up by 20%,etc.), support resource identification (alert people within 10 miles ofthe addict when X occurs), and context setting (e.g., locations withwalking distance of home—half a mile—are considered safe/home location),etc. If an addict is within 1000 feet/5 minutes of a park, then awalking/running excursion can be added to a list of potential actions.And so on. Virtual locations can also have a wide range of uses, e.g.,special Reddit groups, as could quasi virtual/physical locations such assafe zones for Anxiety sufferers within the broadcasting distance of acoffee-shop beacon, that provides for virtual or physical meetings ofsufferers only within range of the beacon.

In various embodiments, a variety of sensors, devices, and mechanism,may be used to determine the location and particularly the context ofthe addict. FIG. 2, for example, describes a device 200 an addict mighthave on their person (e.g., a smartphone, etc.), wear (e.g., in the formof a watch), have implanted, or otherwise be on or near an addict. Thisdevice 200 could contain a variety of sensors 212, such as sensors thatdetect and capture sounds, images, video, or body conditions, forexample. FIG. 2 provides a detailed (but not exhaustive) list of suchsensors 212, including Blood Pressure sensors, Breathalyzers, BloodAlcohol Content sensors, Environmental/Weather sensors, Skin Temperaturesensors, Olfactory (smell) sensors, Bio/gestation sensors, Vestibularsensors, Kinesthetic sensors, and SightNision/Optical sensors to name afew. Other sensors may also be used including sensors for WaterQuality/Pressure, Chemical/Gas/Fire/Smoke/CO2/Flood, Level, Gyroscope,(Passive) Infrared, Eddy current, contact, Ultrasonic, Images, Alarm,Doppler; Fiber Optic, Occupancy, Reed, Touch Switch, Magnetic,Inductive, Microwave, Radiation, Parking, etc.

The use of these various sensors can be to aid in the detection and/ordetermination of the addict's context, e.g., what the addict isexperiencing, feeling, even thinking, etc. Even further, to the extentpossible individually or in combination with other sensors and/or data,the goal of such sensor use can be to detect/determine what trigger(s)the addict may be experiencing, and to what degree, in order to gaugethe risk of relapse and factors involved in the potential relapse, andto identify and set in movement a course of actions that will preempt orprevent an addict's environment from deteriorating to the point ofrelapse.

FIG. 2 discloses one such device(s) 200 that can sense, monitor, and/orcontrol aspects of an addict's context. The device 200 includes an arrayof capabilities, including sensors for detecting or anticipatingaddiction trigger conditions (e.g., contexts, situations, circumstances,environments, and/or state of mind(s) that may cause the addict torelapse or use substances or activities related to his/her addiction,etc.); mechanisms for interfacing with the addict includingtangible/tactile Interfaces 201 (Display, Lights, Sound, Vibration,Heat, Smell, etc.). For example, a Smell Interface could generate thesmell of fresh pine trees or pine tar in response to high riskassociated with the Escape trigger being detected (helping remind theaddict of good times he has had when hiking in the Rocky Mountains) Thewide variety of interfaces is premised that dissemination of informationto/from an addict needs to be in the most effective means possible atany given time or context, which can vary from day-to-day orhour-to-hour. In addition to traditional interfaces such as sound orvibration interfaces, some exemplary embodiments of the presentdisclosure include the ability to project (or interface to a projector)2 or 3 dimensional images, video, GIFs, or real-time holographicprojections that the addict can converse with. It also includes theability to augment reality (insertion of images not actually present),and even (de) augmenting reality, such as the elimination of addictiontriggers/temptations such as liquor stores from the addict's vision.

FIG. 2 depicts a partial list of a variety of sensors that can be usedto determine the addict's context. These sensors can be usedindividually, in combination, and/or with other information about theaddict, environment, situation, circumstance to determine the addict'scontext, as well as determine if there are any triggers being activatedor in the process of doing so. Simple examples include using acousticsensors to detect the volumes and type of sounds in an addict'svicinity, to potentially identify triggers such as Noise (e.g., too highor low volume, etc.), Children (e.g., detecting high-pitched voicesand/or multiple children, etc.), or Social Situations (e.g., multiplevoices in the background, etc.).

The addict's device 200 shown in FIG. 2 may be configured to serve as alocal controller of information of, in, and around the addict. Towardsthat end it is, to the extent possible, self-reliant and contained inhow it collects, processes, and disseminates data-based and physicallybased actions. To begin with, it provides a set of tangible/tactileinterfaces for interacting with the addict, under the assumption thathaving good preventative actions is only part of the battle—such actionsmust be presented in a manner acceptable/receptive by the addict. Veryoften this is dependent on the context of the addict; for example, atnight an addict may be most receptive to audio-based messages, while inthe day he or she may be most receptive to visual-based actions. Inpublic places the addict may not want either, but instead be physicallypulsed/shocked/vibrated/or heated to remind them they are increasingtheir risk of relapse for example (one addiction reality is that manyrelapses are not deliberate, meaning they are a culmination of smaller,even innocent-seeming behaviors that rapidly culminate in a relapsesituation before the addict was even aware they were in danger. Smallshocks or other physical cues can help wake up the addict early in theprocess and stop the danger before it starts to build out-of-control).

The device 200 in FIG. 2 has a variety of capabilities to beself-sufficient and help monitor/manage/control the addict. The device200 has a Locator Unit 202 that has or interfaces to a variety oflocation determination technologies (e.g., GPS, Wi-Fi, CID, Beacon,TDOA, etc.). The device 200 has at least one CPU 203 and associatedmemory, storage, and calculating circuitry, hardware and software, to dodata collection, analysis, and decision making. The device 200 hasspecialty processing and display/interfacing capabilities for managingsuch capabilities as virtual reality, (de)augmentation, holograms 204 a,device effects controller 204 b, and robotics 204 c. The device 200 hasa mechanism 210 for detecting and interfacing with other nearby devicesto extend its sensor/data collection capabilities (for example, tappinginto a nearby security camera to see the addict's surroundingenvironment). The device 200 has an onboard medical controller 207 thatcan interface/integrate with attached and/or embedded devices orphysical wraps, wearables, implants, and/or drug dispensers, in orderto—if needed—suddenly inject or offer for immediate consumption anaddiction treatment drug. The device 200 has a User Interface detector208 that can tie into nearby systems if such systems are deemedadvantageous for delivering a message (for example, broadcasting themessage over a car's stereo system while it is playing a song (e.g.,interrupting the song), instead of ineffectually playing on the addict'sphone). The device 200 has a digital assistant and/or interface to adigital assistant for managing the addict's day-to-day, hour-to-hourschedule. The device has a self-contained module for storing andmanaging the addict's triggers (particularly useful if the addict is cutoff from the broader system). The device has an onboard manager for allthe myriad of sensors and tags on or nearby the addict so such data canbe analyzed locally. Similarly, the device has a beaconinterface/manager 209 c to transmit/receive information from localbeacons. More broadly, the device 200 has the capability of determiningcontext locally, particularly in its ability 210 to tie into localnetworking connections such as Wi-Fi, RFID, RTLS, Bluetooth,peer-to-peer, Internet of Things, mesh/ZigBee, security systems, andother local-oriented networks. As shown in FIG. 2, the device 200 may beconfigured for connection and communication with companion technologies,such as imbedded/inserted/attached addict data/tag/devices 260, supportnetwork devices and/or applications 270, nearby cameras, videostreaming, beacons, sensors, context data sources 280 (e.g., cars,buildings, people, specialty information broadcasts, etc.), sensorarrays 290, Internet of Things (IoT) sensors, devices, and/or networks295 (also shown in FIG. 2b ), etc.

Other capabilities include an SOS button 211 or similar mechanism thatthe addict can push/activate when he or she is feeling particularlyvulnerable to relapse, which will in turn activate other portions,aspects or features disclosed herein. The device 200 also includesmechanisms for causing various levels of physical pain or discomfort(called a reinforcer 206) and/or pleasure, such as a sharp stingingsensation or warm/caressing sensation, that can be used to reinforce ordissuade certain behaviors, with the intention of preventing suchbehaviors and/or associating addiction-related behaviors or contextswith the pain or discomfort. The device also has a variety of identityverification/privacy protection mechanisms for protecting and if needbe, disabling the device and preventing anyone from accessing the dataon the device. There are also user-controllable/definable capabilitieson the device, either programmable or insertable such as SIM-like add-onsensors.

The above does assume an all-in-one device for such capabilities. Suchcapabilities could be spread across multiple devices on and/or near theaddict. Example of these device extensions are specializedaddiction-related wearable and implantable devices that focusextensively or exclusively on detecting and reporting certainaddiction-related conditions, not unlike today's Fitbit, such asbracelets that do double duty as a fashion accessory and blood pressureand skin temperature monitor. These readings could be prompted by and/orreceived by the Addict Monitor/Controller (AMC) shown in FIG. 2, or theextended device could do self-contained monitoring of those conditionsand alert the AMC when they reach a concerning level. Smartphones withembedded alcohol detection sensors is another example. Theseaddiction-specialty devices could continually monitor addiction-relatedconditions which could then send alerts and data (including locationdata) to the addiction server when conditions warrant. Such deviceextensions could include permanent devices not unlike today's paroleeGPS monitoring ankle bracelets. A similar form could allow such actionsas court-ordered alcohol use monitoring for DUI (Driving Under theInfluence) offenders, or even voluntary usage by persons committed tobeating their addiction but needing extra external discipline to achieveit.

In fact, the use in Parolee Tracking and Monitoring Systems is anexemplary embodiment of the present disclosure in at least two respects.The first addresses the limitation that current GPS-based anklebracelets have in tracking and monitoring parolees and other prisoners,and that is its limited effectiveness inside a building. GPS does notwork well inside buildings or in other contexts where there areobstructions between the bracelet and the GPS satellites, such as treesor metal containers, etc. Exemplary embodiments may utilize a variety oflocation-determination technologies and methods to monitor the locationof the parolee. It then goes further to monitor the behavior of theparolee.

Of course, the devices and associated sensors and capabilities are onlyeffective if they are actually powered on. It is an unfortunate fact ofaddiction life that many don't want to become sober, or at least feelthey can be so without any outside help. In any event, it is assumedthere will be the temptation to tamper with/otherwise disable suchmonitoring devices, and includes the ability to determine whichaddict-related devices are active (powered on and in the proximity ofthe addict) and which are the primary one(s) in use at an given placeand time, via monitoring of usage as well as determining which device(s)are in the best position/proximity to monitor the addict's behavior,risks, and actions. This is important as well in general contextdetermination, as people are trending towards having multiple devices,only some of which are actively on or near the person at any given time.In particular, it may be important to determine the primary device whendetermining what the best interface is for communicating with an addictat any given time.

The above capabilities can be packaged in a variety of form factors,ranging from being worn on the wrist or neck to glasses form to evenimplants. Form factors include but are not limited to wrist/ankledevices, wearables, implantables (devices implanted/embedded in theskin/body), clothes, accessories, wallet, Google glasses, Snapspectacles, heads up display, augmented reality displays, inserts (e.g.,ear), FOBs, key chains, are some of the form factors, Siri personalassistants (Built-In, Included, Add-on), smartphones, tablets, laptops,personal digital assistants, etc. The capabilities shown in FIG. 2 donot have to all be in one device but can be spread across many differentdevices—even ones with no physical contact with the addict (such assecurity cameras, local RTLS beacons, etc.).

FIG. 2a provides examples of distributed sensor deployment, datacollection options, localized sensors, and localized networks that maybe used in exemplary embodiments. Form factor/sensor placement 1 mayinclude a hat or headband. Form factor/sensor placement 2 may includeglasses or a visor. Form factor/sensor placement 3 may include earplugs,earpieces, earrings, etc. Form factor/sensor placement 4 may includeimplants/inserts (e.g., teeth crown, pacemaker, ID chip, medicinedeployment, etc.). Form factor/sensor placement 5 may include anecklace, piercings, nose rings, tattoos, etc. Form factor/sensorplacement 6 may include a shirt, blouse, jacket, coat, etc. Formfactor/sensor placement 7 may include accessories (e.g., pens, pocketprotector, umbrella, cane, tools, touchpoints such as buttons and lightswitches, etc.). Form factor/sensor placement 8 may include a ring,bracelet, jewelry, etc. Form factor/sensor placement 9 may include apurse, briefcase, etc. Form factor/sensor placement 10 may include oneor more sewn-in sensors, etc. Form factor/sensor placement 11 mayinclude a zipper, belt, buttons, etc. Form factor/sensor placement 12may include pants, a skirt, etc. Form factor/sensor placement 13 mayinclude underwear (e.g., a bra, etc.). Form factor/sensor placement 4may include a wallet including payment methods, etc. Form factor/sensorplacement 15 may include shoes (e.g., a heel of a shoe, etc.). Formfactor/sensor placement 16 may include a smartphone, tablet, laptop,PDA, FOB, key chains, etc. Included in the form factor/sensor placementmay be the deployment/use of Artificial Intelligence (AI)assistants/Digital Personal Assistants such as Apple Siri, Amazon Alexa,etc. Sensors may be included/attached in such assistants. Conversely, AIassistance functionality may be embedded/attached to a variety ofsensors and/or sensor form factors.

Also shown in FIG. 2a are localized Sensors, information stores,readers, transmitters, broadcasters (e.g., individual rooms, nearbypersons, environmental sensors, etc.), Internet of Things (IoT) sensors,devices, and/or networks. FIG. 2a further shows localized networks thatmay collect/disseminate local contextual information (e.g., multiplerooms, malls, campuses, stores, schools, office buildings, etc.),Internet of Things (IoT) sensors, devices, and/or networks.

FIG. 2b provides examples of internet of things (IoT) addict-relatedsensors, devices, and networks 295 that may be used in exemplaryembodiments. As shown, the IoT addict-related sensors, devices, andnetworks 295 may include smart home sensors, devices, and networks, suchas home controllers, window/door, garage doors, HVAC, lighting, kitchen,security systems, appliances, computers and media, furniture,furnishings, media, landscaping, TV, decorations, rooms, pets, traps,fireplaces, etc.

The IoT addict-related sensors, devices, and networks 295 may includesmart vehicle, connected vehicle, driverless vehicle sensors, devices,and networks, such as cars, trucks, aircraft, trains, boats, RVs/recvehicles, etc. addict/support resources are using, in, within, ornear—sensors, devices, and networks that provide location/context usageof such vehicles and directly or by inference usage and mindset ofaddict and/or support resources and activities to help detect,anticipate, prevent or mitigate high relapse risk situations, such asdeactivating manual driving capabilities and activating driverlesscapabilities (e.g., for drunk drivers, etc.).

The IoT addict-related sensors, devices, and networks 295 may includenearby human sensors, devices, and networks, such as nearby (to theaddict and/or support resource) person(s), devices, networks andsensors—including proximity and/or access to person(s) et al. andcontextual data on, in or near that person as well as groups of personsand activities to help detect, anticipate, prevent or mitigate highrelapse risk situations.

The IoT addict-related sensors, devices, and networks 295 may includesmart retail/activity sensors, devices, and networks, such asrestaurants, stores, banks/ATMs, arenas, gas stations, gym, parking,amusement, hospital, gym, etc.—places persons (addict and/or supportresources) might shop or otherwise spend time in, including indicatorsof items being considered, purchased, and/or used (e.g., liquor, etc.)and activities to help detect, anticipate, prevent or mitigate highrelapse risk situations.

The IoT addict-related sensors, devices, and networks 295 may includesmart office, work environment sensors, devices, and networks, such astemperature, entry/exit, security, work-activity related, stress (mentalor physical)-related, productivity-related, co-worker, office/workarea-related (e.g., conference room lighting, temp, A/C & Heating,Vending, Smoking machines, energy savings, bathroom, security cameras,lights, etc.).

The IoT addict-related sensors, devices, and networks 295 may includesmart city sensors, devices, and networks, such as public spaces andinfrastructure with associated sensors, devices, and/or networks (e.g.,that addict/support resources, etc.) including parking, meters,advertising, police, first responders, etc.) that are in proximity of,connected to, and/or associated with that provide location/contextualinformation about addict, support resources, and activities to helpdetect, anticipate, prevent or mitigate high relapse risk situations.

Some exemplary embodiments of the present disclosure may include orinvolve the collection of large volumes of addict-related data, which isto be handled in terms of volume of data as well as the protection andsecurity of that data and in particular the identity of the addict. Thesecurity and privacy of this data is protected in various embodiments,as the potential for abuse is huge given that in some instances anaddict's location and context may be tracked nearly 24/7 at times. Inaddition, the social and professional stigma of addiction remains hugein our society, and many—even most—addicts greatly prefer theiraffliction remain private. Accordingly, a variety of systems and methodsare provided for addressing such considerations, including but notlimited to limiting the length of time data is stored, physicallydistributing the data among different databases including havinglocation/super localized, context-specific, and/or trigger-specific datastores, invitation-only data access methods, time-limited networking,and encrypting and/or anonymizing such data so it is very difficult ifnot impossible to link addiction-related data to a specific addictidentity. Such protections may include unusual protection mechanismssuch as location selective availability coding (deliberately introducingerrors into location calculations) or “nuclear football” keys where thecodes for unlocking/decrypting data change daily and/or under physicalprotection (e.g., not stored where it can be hacked online).

In various embodiments, a learning engine is provided that utilizesartificial intelligence and other learning algorithms and methods tolearn from an addict's behavior and to refine various systems,algorithms, and processes, such as an addict's likelihood of relapse,effectiveness of actions taken, and types and frequency of datacollected. FIG. 3 depicts an example process for assessing an addict'srisk of relapse and determining potential actions and support resources,with FIG. 5 providing more detail on how such an assessment could occur,and how the assessment algorithm may be modified as more data pointsabout the addict's behavior become available.

By way of an example relating to an addict's Anger trigger, an initialapproach to detecting an Anger condition may initially be based solelyon blood pressure (e.g., a spike in blood pressure is indicative ofanger). However, learning mechanisms disclosed herein may find that fora particular addict the correlation between a spike in blood pressureand an anger condition is low. Instead, the mechanism may determine thatthe addict's anger is immediately preceded in a rapid rise in skintemperature, and is exacerbated when loud noises (e.g., yelling, etc.)is in the immediate vicinity. Thus, the addict's anger-related sensorreadings will change from monitoring blood pressure to monitoring skintemperature and noise levels.

Indeed, various embodiments provide leveraging of numerous data setsabout, related-to, or of potential value to the addict, and the use ofdata analytics, algorithms, and analysis engines to aggregate, compile,assess, analyze, synthesize, and otherwise bring together disparatepieces of information (many location-related) that can be used in theaddict's treatment. Data/data sets can include but are not limited toinformation regarding the addict's medical history, personal profile(e.g., friends, hobbies, etc.), schedule/calendar information,historical data (often location-based) that describes past actions andbehaviors, key enablers (people/places/things that can aggravate theaddiction), key usage triggers, and sources of addiction (e.g., liquorstores, drug dealers)/Points of Interest (e.g., bars, casinos) that theaddict has been known to frequent and/or has demonstrated vulnerabilityto in the past. Such information could be obtained in many ways,including but not limited to directly from: the addict (e.g.,questionnaires, etc.), the addict's support network, friends and family,medical data, historical behavioral data, public records, news media,social media, school records, etc. The communications links used toobtain this information can include, but are not limited to, theInternet, wireless and wireline networks, cloud sources, crowdsources,peer-to-peer networking, sensor networks, machine-to-machine networking,smart homes/neighborhoods, and electrical-grid based networks.

Such data sets and other information may be analyzed on multiple levelsusing analytics that include but are not limited to a Usage Trigger,Potential Response Analyzer, Risk Prediction Algorithms, and anAction/Coordination Engine (many of these concepts are illustrated inFIG. 1 in the Addict Data and Analytics and used implicitly orexplicitly in many of the Figures). The Usage Trigger and PotentialResponse Analyzer takes as inputs the addict's usage triggers (definedby techniques such as questionnaires or psycho-therapy), and theaddict's personal profile and schedule/calendar, as well as historicalinformation about the addict's movements, actions, behaviors, andhobbies, to develop a risk assessment and prediction algorithms aboutthe addict's vulnerabilities to future potential addiction-relatedsituations and develop a series of potential responses. Risk PredictionAlgorithms utilize information about the addict's currentsituation/location/context, addiction triggers, and historicalbehavioral data to develop a risk score, rating, or level (score) forthe addict. If the risk assessment score reaches or exceeds a threshold,and/or falls within a certain range, the Action Coordination Engine willdevelop an action or course of action that will then be launched, suchas contacting members of the addict's support network, rearranging theaddict's navigation (away from high-risk locations), or disabling theaddict's vehicle and arranging for alternative transportation, as a fewexamples.

In various embodiments, an action/coordinating engine (based on theAddiction Server, one or more of the Addict's devices, via a cloud, orsome combination) is provided to coordinate various actions on behalf ofor in the interest of the addict. This action/coordination engine(action engine), the use of which is illustrated in several drawings inparticular FIGS. 6, 6A, 7, and 7A), is responsible for identifying,determining, and/or managing a variety of actions that the addict, theaddict's support network, or others can take on behalf of the addict,usually in response the detection of a relapse risk or actual relapsesituation. Such actions could be automatic in nature, such as the enginedirecting the addict to move to a sanctuary location for example, suchas a nearby AA meeting getting ready to start, in response to ahigh-risk alert generated in various embodiments. An action could bemore of a coordination function, such as coordinating a meeting betweenthe addict and a nearby addict sport person at a nearby meeting in anhour, action could include interfacing with the meeting place (e.g.,restaurant) reservation system, as well as identifying nearby availableparking, and providing instructions to the addict's and the supportperson's cars to activate the self-parking application once in range.This is an example of communicating/coordinating with a variety ofthird-party applications and services that will make the action(s) go assmoothly and hassle/aggravation-free as possible, as a key to resolvinghigh-relapse risk situation without relapse is often to the addict in asa tranquil, trouble-free mindset as possible.

Other actions include obtaining alternative transportation for theaddict, modifying navigation applications to avoid areas whereaddictions can be enabled (such as liquor stores for an alcoholic),and/or posting on social media that the addict is in a high-risksituation. Note not all actions may be reactive, or reactive only toonly high-risk or in-progress relapse situations. The action engine maymanage the selection and communications of daily morning motivationmessages to the addict or perform an alert reminder function to one ofthe addict's support persons reminding him do to his weekly Friday callto the addict.

An often underappreciated and overlooked aspect of successful addictiontreatment is the use of rewards mechanisms associated with good ordesirable behavior. While addiction outsiders often take the attitudethat avoiding the destructive aspects of addiction should be motivationenough for an addict to get and stay sober, the reality is that for manyaddicts that is not enough. Indeed, the destructive aspects of addictionget progressively less of concern to many addicts the longer they areaddicted. However, the prospect of a reward for good behavior (notrelapsing) can have a very stimulating/powerful effect on someaddicts—thus, various embodiments provide a method and apparatus fordetecting/determining good behavior and rewarding such behavior. Forexample, as shown in FIG. 9, a rewards mechanism is provided under thepresumption that many addicts' need a positive-enforcement mechanism asa deterrent to relapse. For example, engaging in good behavior such asavoiding trigger aggravating locations and contexts earns points, asdoes attending (in person or virtually) addiction community meetings.The points accumulate and can periodically be redeemed for (usually)addiction-related goods and services, such as passes to (non-alcoholserving) movie theaters or deep discount coupons for safe hobbies suchas gym memberships or sewing supplies. In contrast, risky behaviors suchas visiting with bad friends (friends that very actively drink forexample) could results in points being deducted.

More broadly, various embodiments include rewards for behavior thatinhibits or prevents addiction usage/relapse (e.g., good behavior),and/or behavior that specifically avoids relapse or the possibility ofrelapse (e.g., bad behavior). While ideally the addict will beself-motivating in his/her desire to get sober, the reality is that manyif not most addicts need some sort of external motivation andreinforcement—both positive and negative—to get and particularly staysober. Thus, a sobriety rewards program can be an integral part ofvarious embodiments. For example, the addict may accrue reward pointswhen he or she spends time in a new (good) hobby, or exercises for atleast 30 minutes a day. Similarly, points may be earned if the addictdoes not go near vulnerable locations such as high drug areas, badfriends, or liquor stores for at least 30 days. An example embodimentwould detect and track these good behaviors through functionalitydisclosed herein and/or data obtained from interfacing with otherapplications and devices used by the addict or his/her support network.Rewards could be in many forms, such as points redeemable for goods andservices, discounts on using an embodiment of the present disclosure orrelated applications, free tickets to an event (presumably but notnecessarily safe events), or even direct cash credits to their bankaccount or mobile pay account. Note a further feature of the presentdisclosure may be to detect/prevent the use of any financial transactionfor the purchase of substances negatively related to the addict'saddiction. Thus, purchases from liquor stores would be detected andrejected via interfaces with the addict's financial accounts andtransaction enablers, e.g., credit/debit cards, mobile pay and bankaccounts, etc. Similarly, credits could be posted to these accounts.Credits could be selectively posted for good behaviors, such as payingfor new hobbies, free Uber rides, etc. Similarly, bad (or not good)behavior could result in points/financial deductions from the addict. Inone example embodiment, a reward engine would determine theapplicability and value of such behaviors, track suchadditions/deductions, and coordinate with addict-related third-partyaccounts.

Not all motivation-enhancers will be rewards-oriented; some need to beencouragement-oriented. This would include words-of-encouragement,testimonials, thoughts-for-the-day, and other types of motivation orself-esteem building messages. Thus, various embodiments provide ways ofdetecting when such messages would be particularly timely, selectingappropriate messages, and delivering them to the addict at theappropriate time, context, and using the best method for encouraging theaddict to get on/stay on the path to sobriety. For example, anembodiment of the present disclosure may determine that the addict issusceptible to the Time-of-Day trigger, when he/she particularly desiresdrinking at 5 p.m. every day. Starting at 4:30, motivational messagesand testimonials may be sent every 15 minutes, such as “life isshort—make the most of it!” This could be sent using any number ofmethods, including text, MMS, email, (pre-recorded) phone call,internet/(private) social media, and other methods. The embodiment couldalso automatically create rotating schedule reminders to (random orpreviously selected) various members of the addict's support network sothat those members could connect real-time with the addict to offerwords of encouragement during these vulnerable times-of-day.

Sometimes actions require more than a positivereinforcement-orientation, meaning a more urgent, aggressive approachmay be needed. One such scenario could involve the detecting of theaddict relapsing while at a bar and having driven himself there. Theaddiction server will have monitored the addict's smartphone forexample, showing that both it and the addict's car have been followingthe same path and has stopped at a local bar. Sensors on the addict'sclothing will have detected the presence of alcohol, and other sensorsnoting a risk in blood pressure and blood alcohol content. Such data maycause the Risk/Prediction engine to generate a very high alert—addicthas been drinking. If no addict support person is in the immediatevicinity to come get the addict, the action engine may elevate theurgency and send signals to the addict's vehicle that disables it (analternative would be putting it into self-driving mode if such optionwas available). Concurrent with disabling the call would be requestingan Uber/taxi ride dispatched to the bar, with an alert to the addictthat such a ride has been arranged for, with the details showing on theaddict's smartphone screen. To make it clear that the addict needs toget into the taxi, the action engine may also inform the addict that oneof his support networks will be meeting the taxi at his home in 30minutes, and he is expected to show at that time. Since this meetingwould be considered a penalty meeting, the addict will lose points fromhis sobriety program if he is not punctual.

Numerous communications methods to/from the addict and other resourcesare used in various embodiments of the present disclosure. These caninclude (but are not limited to) text/SMS/MMS, voice calls, email,social media, video, peer-to-peer and machine-to-machine communications,instant messaging, voice messaging/mail, 3rd party applications,heads-up-displays (such as Google Glass), hologram projections, andother applicable voice and data methods and mediums. For example, theserver may alert via text someone in the addict's support network thatthe addict is nearby and should be contacted because of a detection of ahigh-risk situation. The support person may send the addict an MMS withan uplifting message, and also send an invite to the addict via a 3rdparty application to meet him at a local restaurant at a certain time.The addict could respond with an instant message thanking the supportperson and accept the invitation via the 3rd party app along with a notethat the addict will be approximately 15 minutes late. The supportacknowledges this via a return text but hits an option on his device toinstruct the addiction server to increase the location monitoring of theaddict to detect if he/she takes a relapse-type action (such as stoppingat a liquor store). These real-time updates may be transmitted to thesupport person in the support person's car system display/voice systemor augmented reality sunglasses on the way to the restaurant, so he/sheknows exactly what the addict has been doing since the original alertwas issued.

As introduced above, a key element present in many embodiments of thepresent disclosure is the use of location technology such as mobiledevices and associated GPS or other location determination technology totrack or monitor the location of the addict. The addict's location canbe continually, periodically, occasionally, or on an ad-hoc basiscompared to numerous other individuals and/or data sources to providelocation-related assistance to the addict in continuing his treatment orin avoiding succumbing to the temptation of his or her addiction.

Another feature or aspect of the present disclosure is the use ofunaugmented reality technology. Instead of inserting objects into anaddict's environment (such as a Pokémon character), instead an exemplaryembodiment includes performing a continuous, real-time or otherwisetimely monitoring of the addict's environment, detecting threateningobjects such as advertisements for alcohol, and blanking out orotherwise hiding or obstructing the addict's ability to perceive suchthreatening objects. This could range from removing such objects from anavigation screen (such as liquor stores) to actual blanking orreplacing said object within a Google Glass or Snapchat Spectacles orother Virtual Reality interface.

Another feature or aspect of the present disclosure is the developmentand use of risk scores of relapses or other adverse treatment situation.Such scores could be in the form of a number (like credit scores), ahigh/medium/low designation, color schemes (e.g., Red, Yellow, Green,etc.), and other scoring and/or range classifications (scores). Suchrisk scores could be for a variety of risk types: general risks,situation-specific, location-related, and/or date/time ordate/time-range specific risks, among others. An embodiment woulddevelop these risk assessments/scores using data/data sets but notlimited to the addict's medical history, personal profile (friends,hobbies, etc.), schedule/calendar information, historical behavior data(often location-based) that describes past actions and behaviors, keyenablers (people/places/things that can aggravate the addiction), keyusage triggers as described above, and sources of addiction (e.g.,liquor stores, drug dealers)/Points of Interest (e.g., bars, casinos)that the addict has been known to frequent and/or has demonstratedvulnerability to in the past.

Such scores would be developed/calculated continuously, periodically,ad-hoc, or on-demand, as well as when certain individual conditions aredetected by various devices and/or combination of conditions, as well asdevice-independent conditions. An example of a device-independentcondition would if it were detected by external data sources that theaddict was facing a heavy traffic jam on his usual route home at theusual time. Knowing from his trigger profile that this situation couldactivate the Anger trigger (e.g., road rage), the Risk Assessment Enginewould calculate a high-risk score. This score, in turn, may be used invarious other aspects of the present disclosure.

Taken together, various implementations of the present disclosure couldmonitor the location of people in an addict's support network—such associal workers or their sponsor in an addiction treatment program—tohave them alerted and ready to stand by to assist the addict if theaddict appears to be heading into a difficult situation, such asphysically meeting an alcoholic if they appear to be in danger ofentering a bar. To detect this kind of scenario, the addict's locationmay be compared to other data sources of potentially dangerous locationssuch as bars or liquor stores (for alcohol addiction), knowndrug-trafficking areas (for drug addiction), casinos (gamblingaddiction), shopping malls (shopping addiction), and so on. If itappears that the addict is possibly going to enter a dangerous situationor area, the nearest person (to either the addict or the place inquestion) in their support network can be alerted to intervene.

In various exemplary embodiments, integration and coordination areprovided with the addict's medical and psychological status andprognosis. Medical and psychologist/psychiatrists can embrace variousembodiments as providing ways to monitor real-time the progress of theiraddiction patients, as well as provide near real-time/real-timeadjustments to medications. For example, an embodiment, in monitoringaddict with an embedded naltrexone dispenser, may start detectinghigh-risk behavior. The example embodiment could inform the addict'spsychiatrist of this behavior, who then may decide that the medicinewill need to be increased in dosage. The embodiment would interface tothe service/application controlling the embedded naltrexone, e.g.,either remote or attached/embedded with the dispenser, increasing thedosage, all potentially in real or near-real time, or as otherwisedirected by the addict's medical professionals.

More broadly, this may entail obtaining or otherwise receiving access toan addict's treatment records, providing updates to those records, andinteracting with rehabilitation, therapy, and/or medical providers. Itmay also entail acquiring access to pharmaceutical/drug treatmentprescription information and being able to administer through theaddict's controller device real-time modifications to drugs administeredthrough the controller.

Such an embodiment could also be used by rehab professionals, inmonitoring compliance of addicts under active treatment, during thoseperiods where the addict is in a halfway house or similar situation,where the addict is no longer being closely monitored by rehabpersonnel, but it is desired to see if the addict can manage by oneselfin a somewhat controlled situation. The example embodiment may monitorthe addicts attached/embedded devices as well as other behavior todetect out-of-desired norm conditions or positive (“good”) behaviorwhich will then provide additional data towards release of the addictfrom treatment.

Some exemplary embodiments of the present disclosure include the use ofcrowd sourcing in generating and disseminating addiction-coping ideasand actions. For example, in FIG. 10 an addict enters a room where an“open” beacon is placed as part of a real-time location services (RTLS)network. In addition to providing location help, the beacon also servesas a repository for addiction-related ideas only for those people whoare “tuned” to that beacon, such as a special ID and/or application thatenables a one or two-way communication between the addict and thebeacon. This could be, for example, only for alcoholics, only foraddicts with the Anxiety trigger, and only accessible if the addict isin physical communications range with the beacon. The addict coulddownload ideas for dealing with Anxiety for example, including specificideas relevant for that specific location. Alternatively, the addictcould upload uplifting messages such as praising the locale for itscalming environment. It could even be used to identify and connectaddicts with similar triggers that are in range of the beacon at thattime, thus allowing ad hoc, spontaneous, unscheduled, or flashinteractions not otherwise possible or likely. In effect, this wouldenable very specialized, private ad-hoc meetings or linkups between twoor more addicts suffering from similar issues, in a safe, public yetprivate, anonymized environment

As noted before, helping the addict avoid relapse into their addictionis not necessarily voluntary. Several of the embodiments of the presentdisclosure are predicated on providing help to the addict without theiradvanced knowledge and/or permission. In various embodiments, noassumptions are made regarding the legality of such assistance, but itis presumed that the embodiment includes acquiring permissions from/forthe addict, either directly or via parental/court-ordered ones on behalfof the addict.

One example embodiment of an involuntary use is tracking of the mobiledevice(s) of an alcohol-related court offender. An extension of GPSdevice tracking for parolees, the embodiment could continually track themovements of a court-ordered person who is required to attend AAmeetings and/or stay away from any alcohol establishments. Theembodiment would correlate the person's movements and report back to thecourt or parole officer to confirm adherence to the court order, oralternatively provide proof of violation of the order. In extreme cases,an embodiment could be configured to report directly to the local policeany situation where addict impairment is detected, along with necessaryinformation to apprehend the addict, e.g., location of theaddict/vehicle.

Various embodiments provide insertion, to the extent possible andappropriate, of inspirations, testimonials, motivational messages, andother positive (or deterring) information into an addict's daily life.What messages/information this would include would depend on theaddict's location and/or context, in order to maximize theirappropriateness and effect. A common theme throughout many of theembodiments is the use of these kinds of messages. The “delivery” ofsuch can come in many forms—again geared to maximizing the ability forthe addict to pay attention to and assimilate such messages—through theappropriate interfaces as well as third party applications such associal media, twitter, Facebook, snapchat, etc.

Various embodiments of the present disclosure provide monitoring of anaddict's physical and mental condition through the use of sensors, suchas wearable ones of even medical sensors embedded in the addict's body.These sensors can be monitored to detect conditions of particular dangeror vulnerability to the addict, such as a spike in blood pressure thatcould be indicative of the addict becoming angry. If this trigger isdetected, a message could be sent to the nearest person in the supportnetwork to alert them and have them contact the addict to arrange acalming meeting. Alternatively, if there is no one nearby, an embodimentcould then interface with other medical delivery systems attached orembedded in the addict to deliver a tranquilizer or dose of acraving-inhibiting drug or other medical treatment Similar actions couldbe taken with implants, prosthetics, or other artificial body/brainparts.

Various embodiments of the present disclosure provide minimizing of theextent of and damage from a relapse of the addiction. This could includethe detection (such as via sensors, mobile device, or support network)that the addict has relapsed. This relapse once detected may set off achain of events such as locking or disabling the addict's personal orwork transportation if he/she gets within 100 feet of it; toautomatically call a taxi or Uber-type service if the addict is in needof transportation (facilitated by accessing the addict's schedule andrelapse contingency management instructions); to alert as appropriateportions of the addict's support network; alert family that the addicthas “used” and is under-the-influence (for substance addiction); ifappropriate automatically lock-out the addict from certainhouses/buildings if the addict comes within a certain range; to alertschool officials if the relapsed addict is detected with a certaindistance of their children's schools; even to alert law enforcement (andhis lawyers) if a breach of a restraining order is imminent.

Indeed, there are many transportation-related embodiments related to thepresent disclosure. As numerous studies show that a high percentage ofviolent crimes (and of course other crimes such as drunk driving) occurunder the influence of alcohol or other substances, there would be manyscenarios under which transportation would play a key role if impairmentof the addict is detected or suspected. These could include involvementof the addict's friends or family (e.g., automatic programing of adriverless car to take the addict to their house),ex-girlfriends/spouses (preventing driving to their location(s),addict's support networks (routing those persons to the addict), evenlaw enforcement (routing law enforcement to the addict's location).

As the above scenarios illustrate, the civil liberties, privacy, and/orsecurity of an addict are considerations to be taken into account invarious embodiments. Accordingly, in various embodiments, a privacyengine is provided that determines the conditions under which theaddict's location/context can or cannot be disclosed or used. In mostsituations, the privacy engine may be under the control of and/or entailthe acquisition of the approval of the addict. For example, the addictmay “pre-approve” an embodiment in which a car is disabled orre-programmed in the event that usage is suspected. Many addicts whoadmit they have an addiction and are trying to become sober will agreeto such conditions. They may recognize that that kind of monitoring andcontrol mechanisms would serve as effective deterrents. In variousembodiments, a privacy engine may prevent override of previousdeterrence approvals in contexts where the privacy engine detectsusage/abuse. But similar to parolee GPS ankle bracelets, the addict maynot have control over the privacy engine in some exemplary embodiments.Instead, it may be under the control of a parole officer, medicalprofessional, or other responsible party (for the latter, for example,the addict may sign power of control over to a trusted family member).

In various embodiments, various differing privacy levels and othercontrol mechanisms may be provided through multipleadministration/authorization levels. For example, an addict may have onelevel of access, and an administrator can have a second, higher-level ofaccess to control/override the addict's choices. There may also beadditional levels of admin/control such that someone such as paroleofficer or medical professional may manage multiple addict's profileswith the same user login information. Although the typical control overany one user's privacy may be controlled by only one individual at atime, it is conceivable that multiple persons may have concurrentcontrol, such as a parole officer and a medical professional.

There may also be the opportunity to anonymize people involved invarious embodiments. Not only may there be times when an addict desiresto be anonymous, even to people within his or her support network, but asupport network person may want to be anonymous to the addict. Anexample of the latter for example may be when there is no one availablein the addict's known network to talk to at a vulnerabletime—particularly if a relapse is in progress and no one in the addict'sknown network is available. In one example embodiment, an addict mayconnect, as a backup, to a general or on-call addiction support personthat may provide someone to talk to, or even arrange to meet if they areclose together.

In various embodiments, obstacles may be introduced to obtaining theaddiction substance or pursuing the addiction activity. One example isin using the information from one or more of the scenarios incombination with the proximity of the addict to transportation sources,such as the addict's car. In situations where the addict has relapsed,various embodiment components may automatically interface withtransportation systems within the addict's reach and/or control todisable them or modify them, for example switching on a driverless carfeature (and preventing manual driving).

In various embodiments, interfaces are provided that are ways ofinteracting with the addict. One common and unfortunately generallyapplicable characterization of addicts is the tendency to be lazy. It ismore accurate to say that because of the effects (or after-effects) oftheir abuse (particularly substance abuse) they have low levels ofenergy, motivation, and attention span. In various embodiments,interfacing—i.e., receiving input from but particularly providing outputto—the addict can be very effective when appropriately provided. Thus, awide variety of interfaces may be provided, the deployment of which isoften context-dependent, and appropriately simple and intuitive for theaddict to use, with minimal actions required on part of the addict.

The use of Siri and other types of personal assistants are anticipatedand included as possible interfaces. For example, if it the addict isdetermined to be at high risk of a relapse while home alone, a programcould be initiated that talks to the addict to ask the addict what isbothering the addict (if the trigger is Loneliness for example and nohuman support person is available), or to suggest a call to mom if atrigger response to do so is high on the response list. The use of suchinterfaces could even be adjusted to use the type of voice (e.g., male,female, English, Australian accent, etc.) that has been determined to beappropriate in the past. The use of the addict's children's voices couldalso be used, as a reinforcer or defense to not to drink otherwise thechildren will be harmed in some way.

The types of interfaces anticipated to be used are wide, varied andutilizing a diversity of technologies. They may range from, e.g.,variations of smartphone interfaces (e.g., touchscreen, high qualityvideo/sound, etc.) to others such as augmented reality, personalrobotics, etc. The goal of using such interfaces in all cases is thesame: to have a significant effect on the addict, which in turn may beprovided by an interface appropriate for that addict in a particularcontext.

The use of augmented reality and robotics can be beneficial in addictiontreatment. With Augmented Reality, particular contexts/situations may bemodified to both see things that are not there, but not see things thatare there, or see different things (also referred to as de-augmentedreality). For example, for alcoholics who see a spike in their desire todrink when they see a liquor store, their Google Glasses or equivalentmay be programmed to block out all words and images of alcohol as theydrive by. An alternative may be to replace the words and images withsomething benign, such as words and images about a charity, or evenreplace them with disgusting words and images along site symbols ofalcohol in order to associate such disgusting words and images (such assomeone vomiting) with the concept of alcohol.

As discussed before, interfaces with third party applications may beprovided in various embodiments. For example, interfaces with socialmedia applications can identify friends who are nearby as candidates fortransactional support needs (e.g., serve as support network substitutesif no other resources are nearby). Navigation applications can bemodified to exclude the location of liquor stores or casinos.Communicating with support resources via Snapchat, WhatsApp, Twitter, orFacebook can be used depending on the best/most convenient way ofinterfacing with an addict and/or their support networks. A filter andprogram can be applied to online grocery applications that may preventalcohol purchases from being made. This type of prevention may beextended such that if an alcoholic nears a local bar, an interface maybe established with the bar's systems that download a Do-No-Serve noticeto the bar owner along with facial recognition information.

In various embodiments, a wide variety of interfaces may be provided tointeract with the addict, support network, and third parties. Suchinterfaces include but are not limited to: Direct manipulation interface(e.g., augmented/virtual reality), Graphical user interfaces, Web-baseduser interfaces Touchscreens, Command line interfaces (e.g., commandstring input), Touch user interfaces Hardware interfaces (e.g., knobs,buttons) Attentive user interfaces (e.g., that determine when tointerrupt a person), Batch interfaces, Conversational interfaces,Conversational interface agents (e.g., animated person, robot, dancingpaper clip), Crossing-based interfaces (e.g., crossing boundaries versuspointing), Gesture interfaces (e.g., hand gestures, etc.) Holographicuser interfaces, Intelligent user interfaces (e.g., human to machine andvice versa), Motion tracking interfaces, Multi-screen interfaces,Non-command user interfaces (e.g., infer user attention),Object-oriented user interfaces (e.g., to manipulate simulated objects),Reflexive user interfaces (e.g., achieves system changes), Searchinterface, Tangible user interfaces (e.g., touch), Task-focusedinterfaces (e.g., focused on tasks, not files), Text-based userinterfaces, Voice user interfaces, Natural-language interfaces.Zero-input (e.g., sensor-based) interfaces Zooming (e.g., varying-levelsof scale) user interfaces. Various mechanisms may be provided forselecting/modifying the interfaces based on the user's context. Suchmechanisms are part of the User Interface Detector/Selector/Interface(UIDSI) unit in the Addict Monitor/Controller (AMC) 200 shown in FIG. 2.

In various embodiments, robots and robotics may be used. A robot couldbe used, for example, to serve as an addiction monitor, controller,and/or enforcer. A robot, sensing or being instructed that ahigh-relapse risk situation is developing, might literally grab theaddict by the hand and lead them to a different (safe) destination.

In various embodiments, scheduling and to-do lists of the addict areutilized, as well as the addict's support network. For the supportnetwork, integrating with any scheduling program that they use (such asOutlook) can assist in determining their availability (and in someinstances location) when an addict risk situation occurs. For theaddict, an embodiment may make a dynamic scheduling adjustment and/oradd or delete to-do items if in so doing so reduces the risk of relapsefor a particular situation. For example, various sensors and otherinformation may indicate that the addict's anxiety levels are rising inthe morning. A schedule may have some high probably highanxiety-inducing appointments in the afternoon, for example a meetingwith an ex-spouse and their lawyers that afternoon. Various embodimentalgorithms may determine that such a meeting is too high risk, andprompt (say via a speech, Siri-like interface) the addict to determineif the meeting should be rescheduled and do so if the answer is yes (insome embodiments it may even be done automatically).

Consistent with simple and intuitive philosophy of the interfaces, anAddict Monitor/Controller (AMC) 200 (FIG. 2) may have a variety of formfactors. Such form factors may include, e.g., being part of a mobilephone, tablet, PDA, or laptop; part or fully of Tablet/Laptop/PDA,implants, wearables, wrist devices, or any number of form factors. Notealso that while the functionality described for the AddictMonitor/Controller is anticipated to be done on the device, it is notnecessary, and could be done on a server, in the cloud, viapeer-to-peer, or some other processing mechanism.

The variety and diversity of interfaces and the ability to detect/selectthem—including specialized interfaces within the Monitor/Controller andinterfacing to other third party devices via their interfaces—areeffective for: 1) determining the context, 2) receiving input from theaddict in the ways most convenient to the addict, 3) providinginformation/output to the addict in the ways most receptive to theaddict, and/or 4) providing the most effective deterrent(s) to prevent(or inhibit) a relapse from occurring.

For determining context, an addict user interface detector/selector(UIDSI) on the addict monitor/controller (AMC) may utilize sensors onthe AMC to detect context-related data. A simple example is using anoptical sensor to detect the addict's light environment, helping toindicate if the addict is inside or out, in a lighted environment ordark. Readings (like loud music) from the audio sensor may be used,e.g., to confirm/modify a conclusion (supported by data from theLocator) that the addict has entered a Rave (an impromptu party withlarge amounts of substances to abuse) and is in a high-risk context.

Various embodiments may include the linking and coordinating of ad-hoc,spontaneous, unscheduled, or flash meetings between two or moreaddicts—who may or may not know each other—who either are a) generallyopen to idea of meeting; b) would like to meet a certain time and/orplace; and/or c) are concurrent/coincidently sharing similar triggerrisk profiles and where a potential solution for a high risk situationis their meeting, preferably in a sanctuary location.

Various embodiments utilize physical locations as part of their riskidentification and/or resulting actions/solutions or coordinationefforts. However, many if not most of such embodiments may also usevirtual locations as part of their description. For example, if anaddict is posting on or in a chat room online (in a situation wheretheir physical location is not relevant, determinable, or near anyoneelse), and making comments indicating a relapse/usage mindset, a riskassessment engine through a social media monitoring module could detectthese comments and generate an alert to the addict's support network. Inturn, those members of the support network who were also on that socialmedia site at the same time (or could quickly log on to it) could thenjoin the addict at that virtual location to interact live with theaddict, to talk about their risk situation and/or active triggers. Inthis sense, the physical location of the addict and the appropriatesupport network person is not nearly as relevant as is their presenceat/on the same virtual location on the Internet that enables them tointeract real-time.

Various embodiments utilize algorithms that assess and determine thepotential of a relapse of the addiction(s) in question. Such algorithmsmay be based on many factors, including, e.g., addict's profile,behavior/context history, triggers, medical data, the current ortrending context of the user, etc. As shown in FIG. 5, such informationcould be utilized in a set of prediction algorithms and/or scoringformulas geared to determining a current or trending degree of risk.

Various embodiments utilize the concept of trending context of theaddict. Various embodiments may attempt to anticipate/predict theaddict's context at some point(s) in the future-10 minutes, 1 hour, 3hours, etc.—in order to identify high-risk situations and proscribe somesort of preventive actions. A simple example is detecting the travel ofthe addict on a route for which a highly likely/only conceivabledestination is a prohibited or forbidden destination, such as a liquorstore, gambling establishment, known drug-dealing area, etc., or relatedforbidden areas such as an ex-wife's house under a court order ofprotection.

The above trending context example is also an example of variousembodiments' integration with other applications. In the above example,the prediction of the addict's destination of a liquor store, gamblingden, or drug haven may be made very simple if the addict programs thedestination into his/her GPS navigation application. An interface may beprovided by one embodiment that would automatically (subject to privacyand/administration limitations discussed earlier) transmit thedestination/route information from the navigation application to theembodiment, allowing confirmation of the addict's destination.

Various embodiments provide integration with third party applications,systems, and processes, e.g., as relating to dealing with high risk oractual relapse situations. For example, in situations where it is deemedtoo risky for an addict to drive a car, an embodiment may automaticallyscan through alternatives to get the addict home or to help, withoutthem endangering themselves or others by getting behind the wheel. Thiscould include automatically integrating with an Uber or Lyft applicationto obtain a ride or interacting with the addict's car to alternativelydisable it, make it switch to self-driving mode (and not allow manualdriving at all), or only switch on if the addict was a passenger. Suchthird-party integration could be extended even further, for example,extending to home security applications (e.g., sending an alert to anex-spouse's house under a restraining order to inform that the ex-spousehas been drinking and take appropriate precautions). It could evenextend, e.g., to automatically informing law enforcement if suchrestraining order geo-fences have been violated.

While the above embodiments emphasize anticipation and prevention ofrelapse, there are also many embodiments of the present disclosure thataddress minimizing damage if usage of the addiction occurs. In oneexample embodiment, alternative transportation mechanisms are leveragedwhen a risk of the addict traveling while impaired is considered high.Beyond and including communicating with the addict's personal vehicle(s)and interfacing with the appropriate systems to prevent the addict fromdriving the vehicle, the example embodiment may also interface withsystems such as Uber or Lyft to automatically request ride services whenit detects that the addict is in need of such transportation. It mayalso interface with driverless cars or cars with that option. Forexample, if the addict's personal vehicle has a driverless option, theexample embodiment may automatically switch the car into that mode toprevent the addict from taking control. An alternative may be tocompletely disable the addict's car, or any other car the addictattempts to drive (such as those with embedded breathalyzers or similardriver condition monitoring/car interfacing technologies).

Other transportation-related embodiments include, but are not limitedto, communicating with rental car companies if an embodiment detectsimpairment; airlines to prevent drinks from being served to the addict.It also includes alerting key parties (such as spouses) when the(possibly impaired) addict is on the way to home and activate if desiredvarious security measures when the addict is with a certain distance ofthe home or other location (such as children's schools) so that measuressuch as automatically locking the home, calling security/police, familyetc. can be taken before the addict arrives at the location.

Another example embodiment of the present disclosure providesinterfacing with the increasingly personalized digital media world.Digital signs/signage may be capable of communicating with personaldevices to detect information about the owner in order to tailoradvertising to the owner's specific needs, preferences, and desires.Other signage looks to market to common denominators with beercommercials and the like—causing distress in some alcoholics. Oneexample embodiment may interface with public digital signage, e.g., toprevent alcoholism-related signs from appearing while the alcoholic isin the viewing vicinity, or alternatively causing uplifting,trigger-soothing related ads or messages to be displayed. The signagefor use by interfaces could be coordinated, e.g., by the AddictionServer or by an application on one of the addict's devices with accessto the Action Coordination Engine.

Various embodiments may address the privacy of the addict and others viaa privacy engine 117 to both protect the addict's privacy—to the extentpossible and/or desirable—as well as the privacy of others involved inimplementing such embodiments, such as friends and service providers. Asthe transportation embodiment above illustrates, implementing restraintson an addict in terms of utilizing third party services and/or informingthird parties of an addict's risk situation can involve privacy concernsand legal considerations. In various embodiments, a privacy engine 117may address such matters while still providing functionality asdisclosed herein. The privacy engine 117 may interact with variousembodiments such as the Action Coordination engine to ensure thatprivacy concerns are appropriately addressed in the selection andimplementation of any course of action.

One example embodiment includes sending alerts to persons in an addict'ssupport network when the addict is near, nearing, and/or stopped at aPoint of Interest—POI—(e.g., liquor store, casino, shopping mall, etc.)and/or known geographical area (e.g., drug dealing areas and sites) thatmay provide a temptation to the addict or indeed is the intendeddestination of the addict. Selection of recipients of such alerts may bebased on any number of parameters such as their location (e.g.,proximity to the addict), time of day, day of week, etc. The triggeringof such alerts could incorporate the use of geo-fences to initiate theapplication(s) and/or trigger various types of functionalities when theaddict enters or leaves the geo-fence. This could help the addict inknowing he/she is being monitored (e.g., is being systematicallysupported by his/her personal, semi-anonymous, and/or anonymous safetynetwork(s), etc.) and/or causing members of the network to activelyintervene to help stop/prevent/advise/console the relapse or repetitionof the addiction event. This could include generating alerts when theaddict is near/nearing, and/or stopped by a person of interest, e.g., aknown bad influence on the addict (e.g., see FIG. 1, etc.), etc.

Another embodiment includes providing alternative routes for navigationapplications (and/or data to navigation applications that can providealternative routes) that modify directions to avoid these kinds ofproximity to the points of interests (POIs) or areas described above andother functionality described below (e.g., see FIG. 1, etc.).

Another example embodiment includes providing information/alerts toaddiction means entities such as casinos and liquor stores when thelocation of the addict is in their proximity, so as to enable on-siteactivities such as refusal of service or counseling to the addict.Various embodiments may provide information to credit/debit cardcompanies to temporarily disable the cards to prevent purchases or cashadvances, and/or disable the usage of other electronic or computerizedpayment mechanisms such as mobile wallet technologies. Variousembodiments may provide capabilities to block individual transactionswhen a part of that transaction includes addiction-enabling products orservices. For example, grocery stores in many states allow the sale ofbeer, wine, and even liquor. It would be very undesirable to block agrocery store transaction by the addict just because the place he wasshopping in sold alcoholic beverages in addition to many othernon-alcoholic items. Various embodiments may provide a capability to tiethe store's check out mechanisms (e.g., bar code scanners and the like)to the mobile payment system as well as to the data profile of theaddict (see below) to detect when an addict (in this case an alcoholic)attempts to buy alcohol. An alert may be sent to the mobile paymentsystem or perhaps directly to the grocery store's check-out system todeny the transaction. The addict would perhaps be given the opportunityto remove the alcohol from the checkout process and continue thecheckout without the alcohol being included to avoid embarrassment andthe like.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an addict with a panic button or hot-keyor equivalent on the addict's device(s) that could trigger somepredefined functionality described herein. Such activated functionalitycould also be based on the location, medical state, and/or othercondition of the addict, or even randomly selected.

Various exemplary embodiments may provide ability for an addict to postblogs on addiction websites describing his/her state of mind and/orother pieces of context, including location, that could help the addictin unburdening themselves and/or cause other addicts to post responseblogs and/or contact the addict directly to support the addict. Variousembodiments may provide linkages to book/article passages or videofootage showing the author(s)/actors in addiction situations and/ordescribing the impact of their addiction and/or showing them inembarrassing situations and the like. Various embodiments may providevarious media reminders of public figures who have seriously damagedthemselves in some fashion, such as high-profile actors or sportsfigures who seriously impacted their careers by behavior that seriouslydamaged their public profile and in turn their careers and finances.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing predictive analysis of potential relapsebased on the addict's historical data, such as travel patterns andhealth data such as blood pressure, and linking them to real-time data(e.g., current location and directions; blood pressure monitors on or inthe person), to anticipate and evaluate the potential for a relapse, andto initiate preventative measures, such as some of the types of alertsabove or alternatives below, or even triggering a release of bloodpressure medicine or other types of medicine that would be deemedappropriate in the circumstances, such as Campral or Naltrexone (e.g.,alcohol containing medications, etc.). These medicines' administrationcould be, e.g., in the form of alerts to the addict to take it urgentlyor as a reminder to a periodic schedule, or even triggering the releaseof the appropriate dosage via implants in the addict, or even moreexotic methods such as being shot with the medicine from a dronefollowing the addict, or providing a supercharged vibrate on the mobiledevice that could serve as a sort of wake up call, or more benignmechanisms to the addict like changing their ring tone to the addictsfavorite (or least favorite song), or launching a song on their iPodthat reminds them of good/bad childhood experiences. Alerts could besent, in various embodiments, to appropriate physicians and/or asupdates to the addict's medical records. Various embodiments may providecapabilities such as functionality during the addict's sleeping periodto subconsciously reinforce the addict's resolve to fight the addiction,such as special programs broadcast next to the addict's bed.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to sensors such as in-vehiclebreathalyzers that can activate alerts and other functionality disclosedherein. Various embodiments may provide capabilities such asaddiction-trained dogs that detect the presence/usage of the means ofaddiction/relapse and take preventative action such as hiding the means(a kind of reverse application of the dog getting a beer for hismaster). Various embodiments may provide capabilities such as affixingRFID tags on addiction means (e.g., alcohol) that trigger alerts andother disclosed functionality if the tags are moved.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing location-based alternatives via theaddict's mobile device to the addict to help either passively oractively in dealing with the addiction in general and/or relapses inparticular. Such alternatives include:

-   -   Alerting the addict when he/she is in the proximity of their        support structure, such being near an active or soon-to-start AA        meeting or other support groups that meets the needs/preferences        of the addict (example: a women-only meeting), or near a member        of his support network such as a sponsor, family member, or        friend; linking to navigation functionality to provide the        addict with directions to the support person or structure;        automatically calling the nearest support person or type of        support person (e.g., family member, etc.).    -   Providing location-based suggestions for other alternative        activities by linking with a personal preference database, such        as a nearby teen/youth center, church, movie theatre with a        movie/movie type he likes that will start soon, bingo, a bowling        alley, book store, coffee shop, and other types of alternative        activities based on a database of preferences, such as        self-esteem boosting activities which may include        volunteering/community service. Other alternatives may include        errand-running based on location, such as going to the grocery        store or picking up the dry cleaning. These could be done in        general while the addict's device is on and/or based on other        parameters such as only during a certain day or time or        geographical location, on a periodic basis, or randomly.    -   Providing location-based advertisements for key products and        services based in whole or in part on preferences and/or        templates for resisting temptation, such as bakery or coffee        coupons of nearby establishments (in this example, sugar and/or        coffee being considered an effective alternative to alcohol).        Could include special sales for that addict only and/or for a        limited time (e.g., 2 hours, etc.) on those products and        services.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes detecting that the addict, having been located athis/her residence for a certain period of time, leaves that residence ina way or timeframe that triggers an alert to said social network orlegal/public authorities. Said alert may preferably have the effect ofeither: 1) deterring the addict from said action (presupposing that thereason for the leaving of the residence is to indulge in the addiction);and/or 2) providing a public safety service.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages with databases storing images ofphotos, videos, text messages, legal transgressions (proven orotherwise), and other testimonial type data that can be used to remindthe addict of the consequences of their addiction, and/or becommunicated to their social safety network and/or other entities,particularly in the prospect of harm or inconvenience to otherindividuals and/or property is deemed possible. These linkages could beof a personal nature, or of an external nature, such as DUI car crasheshaving nothing to do personally with the addict but serving as starkreminders to the addict of the potential consequences of their actions.These images and/or messages could be automatically sent to the addict'sdevice and displayed in the most effect manner to gain the addict'sattention.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to/reminders of loss of marriage,deteriorated connections with children, family, and friends, loss ofjob(s), embarrassing behavior, situations or events caused by theaddict's addiction, negative financial impacts like lost opportunitiesor assets, and other personally negative situations experienced by theaddict. Reminders could be in any form, such as photos, videos, text,testimonials, etc., and rendered to the addict via display, text, audio,3D, 4D+, heads-up display, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to medical databases that showthe effect of addictions of various parts of the body, such as liver,stomach, colon, nose, and other effects such as blood clots, diabetes,etc. This information could/would be transmitted to the addict's devicein various methods, e.g., voice, text, photos, video, or other methodssuch as heads-up-display, direct neural-to-the-brain and/or other bodyconnections (e.g., implants or other methods of directly internallycommunicating with the addict, and other methods). This may includebeing able to display, text, and/or provide verbal reminders to theaddict and/or others in the addict's network. This may also include thepotential for notifications/linkages to other interested parties, suchas the addict's doctors, therapists, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to legal and other public recordsindicating personal or of an external nature of the effects of theaddiction, such as DUIs, restraining orders, and the like. This mayinclude being able to display, text, and/or provide verbal reminders tothe addict and/or others in the addict's network, particularly to thosein close proximity to the addict. This may also include potential fornotifications/linkages with public and private safety personnel andsystems in close proximity to the addict.

Another exemplary embodiment of the present disclosure includes thedevelopment and presentation via various types of user interfaces oflocation-based information about the above in the form of news, alerts,other presentations, etc. This presentation may be motivational and/ordissituational in nature to reinforce the need for the addict to abstainfrom their addiction. For example, a newsfeed could pop up as a bannerad on the addict Jane Doe's phone or Google Glass as she was nearing aliquor store on 5th and Elm Street saying “News Flash: Jane Doe laughsat the temptation of Joe's Liquor at 5th & Elm!—World Rejoices!” Thiswould have the dual effect of a) reminding Jane she is being watched ormonitored, and b) providing real-time reinforcement and praise forresisting that particularly temptation.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes the creation of an anonymous and/or semi-anonymoussocial networks, allowing users (helpers for the addiction that meetcertain parameters including location proximity to the addict) to flagthemselves as available to help (perhaps after achieving some sort oftraining) in the event that no one is available in the addict's personalsocial network, or as a supplement or substitute. These networks can becrowd-sourced from the community at large. The primary goal would be toempowering the addict to know that someone is just an action away if theaddict needs help. The helper could choose to appear anonymously or not,as could the addict.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes detecting, monitoring, and/or preventing purchasesand/or usage of addiction-related materials. This may include providingperiodic, continuous, or random reporting of such purchases and/orpurchase attempts to other interested parties on a specific,semi-anonymous, or anonymous basis.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing incentive programs and frameworks thatrewards the addict for positive behavior, such as coupons for freedinner for two if the addict's behavior reached a certain positivethreshold, such as not stopping at a liquor store for 30 daysconsecutively (verified by the application). This would entail ahistorical tracking/counter/points type system within the addict'sapplication profile. This would entail methods and technologies such asperiodic intensive tracking of the addict for an amount of time, such asa week or a month (or moderate tracking as a regular ongoing part of theapplication) and awarding points (redeemable for prizes) based onverifying going to a certain number of meetings or NOT stopping at aliquor store. This framework also has a lot of applicability for othersinvolved. There can be support quests, where say a child has to write aletter to their estranged parent (a real letter) and if so, they get Xnumber of points, which are redeemable for products or servicesappropriate to that person's demographic, such as an ice cream couponfor children 10-15, or iTunes credit for persons 10-25, with appropriatecontrols to prevent abuse. Another aspect of incentives is abehavior-based pricing model. This could entail raising the monthly costof the service to the addict when the addict exhibits bad or undesirablebehavior (e.g., relapsing, etc.), then lowering the price and/orproviding refunds when the addict exhibits good or desirable behaviorover a certain period of time.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing a user interface (UI) platform to theabove functionality that minimizes or reduces the actions that theaddict has to take for the functionality to activate. Indeed, by usingcapabilities that the user does not have to provide any action toactivate the functionality, particularly the use of the addict'slocation, various embodiments may provide ways that reduce/eliminate anyreluctance on part of the addict to use the functionality. Variousembodiments may provide the ability to customize the user interface forthe addict to maximize the convenience and usability of thefunctionality based on the user's profile. For example, the UI for analcoholic may be much different than that for a prescription drug addictwhich in turn may be much different than the UI for a shopaholic. Theycould vary by many parameters, such as gender or location (e.g., text tovoice conversion could be done in a western, southern, or northeasterndialect depending on the home location of the addict, etc.). The sensorsused for detecting high risk situations may be much different; means ofcommunicating messages may be much different. Various embodiments mayprovide the ability to modify/customize the user interface based onmultiple parameters, particularly location. Other accessory typemodifications to a UI based on location also may be provided. Forexample, if an embodiment detects that an addict (or in this case alsoincludes non-addicts) is in a movie theater, it may automatically switchthe device to vibrate, and provide a capability that inverts the displayof any messages, e.g., instead of a bright background with messages indark colors, the background would switch to a dark one with the messagesin a lighter color, thereby greatly diminishing the impact on the darkmovie theater environment. This capability could be enabled by varioustechnologies tracking the movement and location of the person (addictionnot a requirement in this kind of use case), and other technologies suchas detecting payment for the product or service and using the nature ofthat transaction (e.g., buying a movie ticket, etc.) to automaticallymodify the behavior/user interface of the device. The device could thenbe instructed to revert to its former configuration when the user/deviceis detected to be leaving the theater.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an initial setup capability that minimizesactions and time by the addict to setup his/her profile to takeadvantage of the functionality described herein. A pre-populated profiletemplates may initially be used based, e.g., on a few parameters such asaddiction(s), gender, age, key medical conditions such as depression,medication taken, home/work locations, key hobbies/leisure activities,key addiction triggers, and/or (for some addictions) ethnicity, etc. Inaddition, various embodiments may provide the addict with a number ofoptions for setting up his/her support network. These could includemanual input of individuals/groups (generally undesirable),accessing/downloading individual/group information from otherapplications such as Facebook/social networking apps, phone and/or emailaddress books, local AA meeting participants, etc. The addict would thenneed to confirm each individual (using voice or computerizedconfirmation) before they would be active in the addict's profiledatabase, plus other parameters such as type of support (e.g., alert 24hours/day, only certain circumstances such as only when the addict isout of town, etc.). These individuals may need to be sent confirmationmessages, depending on circumstances (e.g., wanting to send alerts tothat user) and/or the addict's preferences, asking permission to beincluded in the addict's support structure. Other parameters such asallowing the use of anonymous support individuals/entities would berequested. This initial information would then be used to provide theinitial profile template for the addict. This information would includeflagging of location-related/addiction-related POIs, such as liquorstores within a 50-mile radius of the addict's home/work or movietheatres within a 10-mile radius. It would include key directionalinformation such as the various routes between home and work, whichcould then be used to provide alternative routes to avoid addictiontempting locations/POIs and triggers and/or provide convenient access toalternative activities, plus other data.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an ongoing profile modificationcapability, which could be done by one or more means or ways: manuallyand incrementally, such as when the addict is calling, texting, oremailing a person asking the addict if he/she would like to add them totheir support structure. Various embodiments may also provide or have alearning capability, such as detecting changes in behavior or conditionof the addict (e.g., changed driving patterns or elevated bloodpressure, etc.), correlating the changes in the addict's behavior orcondition to relapse risk factors and then modifying theactivation/prioritization/type of functionality described herein.Various embodiments may include suggestions by others in the addict'ssupport network, such as recommending a good restaurant frequented byother addicts, a new bridge game being setup, a club at the addict'shigh school that might be helpful to the addict, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to, usage of, and combination ofa wide variety of databases, including but not limited to:street/navigation databases; POI databases; satellite map databases;addiction group databases, such as AA meeting types, times, andlocations; event databases (e.g., ballgames, concerts, etc.); rehabfacility databases; self-help databases such as WebMD; weatherdatabases; public safety databases (e.g., police stations, sex offenderdatabases, etc.); geographical/terrain databases; highly specializeddatabases such as databases that show hot fishing or surfing locations;plus a host of individual databases such as local theaterdatabases/listings describing the locations, showings, and times of amovie theatre chain's location in the vicinity of the addict's home orwork. This may include linkages to products and services in the addict'sarea that could help deal with triggers particular to the addict. Thismay include sign-up databases for addicts in a certain area who want tobe part of a local, regional, national, and/or international (evenspecial) support network, on a personal, semi-anonymously, or anonymousbasis either offering to be part of a support structure, seeking thesupport of a support structure, or both. These databases could bepublic, private, or both. Various embodiments may provide new and uniquevalue both in the application/usage of the databases, and also incombining/integrating them in new and unique ways.

FIG. 15 depicts an example embodiment of a method for monitoring for arisk of a pre-identified behavior (e.g., pre-identified addict-relatedundesirable behavior, etc.). FIG. 15 also includes example triggers,priorities, and initial risk assessment/detection sensors. As shown inFIG. 15, a first step may include providing a questionnaire (e.g., onpaper, online, video, etc.). Questions on the question may be designedto obtain not just facts but to also elicit an emotional response fromthe person (e.g., addict, etc.) answering the questionnaire. Thequestionnaire may be used to determine the priority/impact/severity ofeach drinking trigger listed as well as any other ones that might apply.A high ranking for a trigger may indicate that the person often drinksor wants to drink when this trigger occurs, whereas a low ranking for atrigger may indicate that the trigger never causes the person to drinkor want to drink.

Continuing with FIG. 15, location(s)/context(s) for the addict may beextracted, such as from computers, phones, social media (includinglocation and time stamp), etc. Location(s)/context(s) for the addict maybe extracted from a third party, such as location, context-rich and/orimage/photo-intensive applications, e.g., navigation, etc. Afterextraction of location(s)/context(s) for the addict, the method mayinclude identifying, assessing, and prioritizing triggers, andidentifying key sensors, determining sensor and other sources (e.g., keyreadings, values, levels, ranges, yes-no parameters, etc.) to monitorfor risk. The method may also include accessing and/or using addict datasources including addict data and analytics 104 (FIG. 1), includingpredictive analytics data, etc. The various possible triggers shown inFIG. 15 are not necessarily independent from each other as there may berelationships between the triggers.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing the ability to anonymize the addict'sidentity for some or all of the above functionality, particularly (butnot exclusively) for functionality that goes outside of the addict'ssupport network, such as providing moment of value mobile couponsdiscussed above that would shield the addict's identity from the companyproviding the coupons.

More broadly, the protection of addict (and their support network)privacy is one aspect of the present disclosure. The personal datacollection mechanisms described in the present disclosure canconceivably be used to track a person's movements 24/7. To preventinappropriate use of data, the present disclosure provides new systems,methods, mechanisms, and techniques particular to how and where the datais collected, and who, how, when, and why it is used.

The present disclosure describes exemplary embodiments of newsystems/methods/techniques to use location and/or contextinformation-based security as a way to protect location and/or contextinformation. An important premise is that such personalizedlocation/context-based images and other prompting mechanisms are readilyfamiliar in some way to the user without the need of memorization. Atthe same time, such images/mechanisms would be very hard to hack orrecognizable by (ro)bots since the ability to recognize them is rootedin the user's experience—not in any sort of logical or algorithmicmechanism. Such security keys, passwords, or other security-relatedelements and mechanisms could be used in protecting the broadercollection of location/context information. Besides the ability toprotect the voluminous addict behavior data, such location-basedsecurity could be used as part of financial account passwordverification or reset processes, or even an extra layer of security toprevent individual household appliance or device hacking or access suchas preventing fake off hacking of TVs, etc. that could become morevulnerable as the Internet of Things becomes more prevalent.

One of the important premises of the location-based privacy and securitycomponents, embodiments, and examples is that location and context,presented in a user-friendly manner, requires little memorization of theimage, unlike say an alphanumeric password. They are readilyidentifiable by the user once presented to them in some recognizablefashion. In particular, location/context can take advantage of theconcept in human memory of recognition versus recall. Recognition refersto our ability to recognize an event or piece of information as beingfamiliar, triggered or prompted from external piece of information orinput. Recall designates the retrieval of a piece of information frommemory without any external prompting or input. Today's passwordsrequiring number, letters, capital letters, and punctuation signs areexamples of account/data/database keys or passwords that rely in totalor significantly on a person's recall ability. Nearly all memory expertsacknowledge that recognition is far easier for most humans than recall.Location-based privacy and security takes advantage of recognition,which besides being easier for a given person it much harder for othersthat have not experienced the piece of data recognized by the user,hence it being much harder to hack, guess, or otherwise deduce,particularly if constrained in some way, such as a time limit, etc.

FIGS. 11, 12, 13, and 14 describe examples of location/context-basedelements and embodiments of protecting/verifying valid users and/oraccess to this information. Broadly, the capabilities illustrated bythese diagrams and associated disclosure and embodiments may be referredto as location-based security and/or location-based privacy, using someform of image(s) as key(s) to locking/unlocking/securing a broader setof information, such as account information and/or location/context datacollected for purposes of preventing or dealing with an addiction riskor relapse. The images are not limited to only visual/graphical items,such as photographics as the images may also or instead be visual,audio, graphical, video-based, photographic, textual, alphanumeric,and/or other types of passwords depending on the user interface. Theimages could be in the form of sensor readings, which often have uniquevalues depending on the sensor. The images could be multi-dimensional,such as two-dimensional (2D), three-dimensional (3D), four-dimensional(4D), or beyond if, e.g., including time/time-lapse/time-projectingimages. The images may be static (e.g., still pictures), dynamic (e.g.,videos, etc.), past or present, based in memory or live streaming. Or,the images may also be combinations/hybrids of the above. In whateverform in various embodiments, the location-based security/privacy imagesare location and/or context-based, experienced by or known to the userand/or person(s) authorized to have access to the broader information.

Location/context images can be obtained, derived, or computed fromnumerous sources. FIGS. 1 and 2 show a variety of location/context datacollection devices, networks, sensors, and other mechanisms and sources.FIG. 11 further describes several example sources and associated typesof data along with examples. FIG. 11 also describes examples ofquestions that can be used to prompt for passwords/keys and/or accessto/resets/verifications of changes/access to key information. FIG. 11provides an example of a user memory profile that can help tailorquestions/potential answers to how that person best remembers/recognizesimages. Images may be a general term that is inclusive of all forms of Q& A methods and user interfaces. Questions can be tailored based on userinterfaces employed, e.g., text, photo, graphics, audio,Virtual/Augmented Reality, etc.

FIG. 12 provides more detail in terms of a method of how such keys mightbe obtained and used; examples of such keys; and algorithm examples forgenerating/developing/answering verification questions. FIG. 13 takesthe two-dimensional oriented algorithms and examples of FIG. 12 to threedimensions, showing how 3D images (e.g., Rubik's-cube type shape, etc.)could be used to introduce more sophistication intolocation/context-based security. FIG. 14 shows a variety of 3D formfactors and breaks out in more detail, data elements that would becaptured and associated with image keys in order to provide morealgorithmic and verification options.

For the figures, it is important to note the parts thatdevices/sensors/networks play in sourcing images, providing access toimages, and in algorithmic processing of such images. A 2D photographtaken by a low-resolution smart phone of a backyard swing set hassimilarities with and distinct differences from a 3D image capture orvideo of that same swing set taken by a 3D heads-up-display camera withmulti-media sound. The device, beyond capturing the time, place, andimage of the location/context, also adds certain contextual elements tothe image. In turn, those elements may play an important role in theability of the user to recognize the image later when it is presentedduring a verification/password acceptance Q & A process.

One of the embodiments illustrated in FIG. 11 is the concept of amultiple-key or jigsaw puzzle-based key or password forlocation/context-based security. The jigsaw puzzle-based informationlock includes multiple keys to be correctly assembled in the correctorder/sequence in order to provide access to any data.

For example, the overall key is that a picture of a house must beassembled. This house has been broken up into 9 separate images with 9separate associated location/context-based keys. Each of the 9 keys werecreated and assigned during a particular time during which certaincontrollers or monitors were on-call for a particular addict.

The Location-Based Verification Examples sections of FIG. 11 describessome of these either as Verification Grid Element # or Grid #. Invarious embodiments, data produced and used as described in the presentdisclosure could be location/context images as actually experienced orseen by the device/user, or they could be representations of alocation/concept. Example of questions or limitations on verificationanswers are also shown in FIG. 11, such as “Where you have been in thelast (week, month, year, etc.)?” The use of multiple devices and/orperspectives of the same image (such as a house, daughter, etc.) wouldmake it much more difficult for a (ro)bot and/or hacker to replicatethrough algorithms, analysis, or even guessing, whereas for the user itwould take very little effort to recall/recognize the correct images. Inthe location/privacy-related examples and embodiments disclosed herein,a user may refer to a person or entity trying to gain access/validationto an account, database, data set, or other piece of information. Or,for example, a user may refer to a person and/or an entity generatingsuch data/information. A user can be a human, computerized entity,and/or anyone or anything that has valid permission to access theinformation involved.

In addition, there are potentially more persons/entities involved inlocation/context privacy and security than just users. Certainly, datacan be generated/sourced from a wide variety ofmechanisms/methods/sources. In addition, control over this data does notnecessarily have to be by the (primary) user or account holder. Indeed,in addiction-related embodiments in particular, different data sets canbe controlled by someone other than the key user (e.g., addict, etc.).One embodiment of such in location-based security is the use of anaddict monitor or controller. Such an entity can be a human (or evenartificial entity) that is responsible for monitoring the addict over acertain time period. The idea is to maximize or at least increase theprobability that if a relapse were to happen, there would be a personon-call that would be at least generally aware of what the addict wasdoing, or at least have no uncertainty that if a high-risk/relapsesituation were to occur that they are #1 on the list of support personsto respond. This entity may be a human or an artificial intelligenceentity that has the responsibility of being at the top of the addictsupport hierarchy during a given time period if the addict were toencounter a high-risk situation during that time. The general purpose ofsuch controllers is multi-faceted: to distribute security of addictinformation across different entities as a general security precaution;make it progressively more difficult for hacker to access the data; and,in the case of addiction-related data provide security control toentities that are (almost literally) more sober than the actual user.

A side aspect of the controller function is that the controller'slocation/context would be sampled or otherwise tracked periodically forthe time-period when they are on-duty. This location/contextinformation, besides being used in identifying/locating support personif needed, could also be used to create a location/context-basedpassword for the addict's location/context—based on the controller'slocation/context—during the period of time the controller was on-duty.

While the location-based security and privacy elements and embodimentsdescribed in the present disclosure are primarily intended to protectinformation gathered in relation to getting/keeping an addict sober, itsuse is not limited to such addiction-related purposes. For example, oneset of embodiments of this location-based security and privacydisclosure is in using recent and/or historical location/contextinformation as a password reset verification mechanism, to prove that auser is not a robot, and/or to verify financial transaction. In one ofits simplest forms, such verification would consist of a system (orperson) asking where a person was on a certain date and/or certain time.Note that this query and response mechanism could be done using any ofthe user interface forms described elsewhere in the present disclosure.

Below is a variety of embodiments and examples that illustratelocation-based security and privacy concepts applicable to many types ofsituations, verification environments, and protections/access tofinancial, addiction-related, or other types of sensitive data. Most(but not all) such example data protection/access mechanisms have somesort of qualifiers to limit the scope of a location/context-relatedquestion or statement.

For example, Grid answers to the question could “Select the location &activities you were doing on July 26, 20XX.” Note that the specificityof the question/statement can be tailored to the User'sLocation/Context-based Privacy and Security (P/S) Memory Profile, sothat questions/statements are not limited too narrowly (or broadly) forrecognition purposes:

-   -   As an easy example, Grid #4 could be an image of the user's        mother's house—a location and associated context (where they        live currently, as it looked before after a remodel last        year)—easily identifiable to the user but less so to others and        unlikely to be the current image in a readily accessible        databases (e.g., Zillow, Google Maps, etc.). Such images could        also be taken from angles (such as the backyard) that are        generally not accessible on those kinds of databases,        particularly since such databases do not generally have linkages        to specifically identified family members and associated        belongings. Additional security could be added that requires the        user to identify how the image was captured (in this case from        User's Device A). It may be that a user may have many devices        (particularly for an Internet of Things user), but only takes        pictures from 1 or 2 devices. This fact might be known only to        the valid user.    -   Grid #5 shows a vacation picture of the user's son on vacation        in Location X doing Activity/Context of mountain climbing, taken        with a Helmet Cam—also easily identifiable to the user but much        less so to others. This verification question statement could be        further qualified by asking for example images that “were on        vacation”, “shows 1 of your children”, “shows mountain climbing”        or “ocean”, “an activity you did 20 years ago”, etc.—knowledge        likely known only to the user, as would the device used, which        could be further obscured by using a nickname for the Helmet        Cam, such as “3rd Eye” or “Gorgon”.    -   Grid #1, shows a picture of a “North Carolina” street sign. The        image may or may not have been “seen” by the user's device;        rather, it is representative of whether the user had (or had        not) been in North Carolina on (or around) the 7/26/XX time        frame. Such an image could be generated from the raw data of a        navigation app (such as the user's car #1—Device B onboard        navigation system), combined with a geo-fence around the state        of North Carolina that generated a database reading when the        user crossed the state boundary on 7/26/XX.    -   Grid #2 shows a textual representation of an address visited on        7/26/XX, such as the street address of the user's mother's house        in Grid #4, derived from that image's latitude/longitude through        a latitude/longitude to address converter program.    -   Grid #3 shows an algorithmic derivative and associated graphical        image that was created from the latitude/longitude centerpoint        of all activity on a certain date. So in this case, the actual        location/context of the user is not displayed, and used in an        indirect manner, but the user could easily associate that they        were a) indeed traveling overseas, b) it was in Australia,        and c) it was on business (a hacker might assume it was for        vacation, which in this case would be incorrect).    -   Grid #6 shows am image that has multiple images of past context,        in this case a picture of an dining room in a former home with a        now deceased pet. This could alternatively be a possible answer        to questions about playing with pets in old homes contexts.    -   Grid #7 is an algorithmic representation of a crossroads that        the user passed in a certain timeframe. Alternatively, it could        be a subtle representation of a location/context, such as taking        a trip with my cousin Sophie to Santa Monica Calif.    -   Grid #8 illustrates how an image is not necessarily        photo/video/visual/graphical in nature. In this case, it is an        audio clip of “California Dreaming” by the Mamas and Papas as a        way of indicating a location or geo-fence, based on location        information taken in this case from a mobile social networking        post from Friend “e”. This illustrates how images do not have to        be directly sourced by or even known to a user—the key is to be        recognizable to the user/person trying to access the        account/data.    -   Grid #9 is an image pulled at random from a sample of retail        visit locations, with the address matched with the store name        and image (source: Internet). This could be in response to a        question of where the user did NOT shop in the last week for        example, or something even more subtle in response to a question        of “select store(s) in a retail strip mall where you've shopped        the last week”, assuming the person knows that there is such a        store right next to a frequent shopping destination he or she        went to, even if they did not go to that specific store during        the timeframe in question.    -   Grid #10 illustrates an image taken from an indirect source (in        this case for example a child in the backseat of a car). The        driver may not have known such an image was being captured, but        enough detail about the driver (user) and his context is shown        that he would be able to recognize key location/context        questions (such as what car was this picture taken from, or what        state was the car in at the time of this picture?).    -   Grid #11 is another example of an externally sourced image—a        satellite image of a house from Google Satellite. Such images        could be asked in a verification process regarding valid family        member homes. It is likely only a user that is very familiar        with such homesteads could quickly identify such homes, making        more difficult for a bot to answer correctly.    -   Grid #12 shows how colloquial references to a location can be        used as a verification mechanism. In this case, like Grid #'s 2        and #4, it is intended to represent the user's mother's home.        This colloquial/textual representation could be used as a        stand-alone answer to a verification question or be paired with        other images such as asking the user to select all images        related to a family member. In addition, a family tracking        application such as AT&T Familymap can be used to extract        colloquial names and locations of key places, such as “Mom's        House”, “Kids School”, etc. These terms can then be        paired/matched to actual or derived images of those locations,        and some or all of the results presented as options in the        Verification Process.    -   Grid #13 shows how the quality of an image (or lack thereof) can        be used as a verification mechanism, under the premise that a        poorer quality image will be more difficult to analyze by a bot        or other hacking mechanism. Grid #13 is actually a much poorer        version of the image of the user's mother's house in Grid #4.        While still recognizable to the user, the image is likely to        just appear as a bunch of squiggly lines to a bot or        non-authorized user. Quality variations could be done in        numerous different ways, including changing image fill/lines        colors or solidarity (e.g., dashes instead of lines), color to        black and white, pixel density, and/or circus minor-type        distortions.    -   Grid #14 shows a three-dimensional image of a roller coaster        ride, illustrating how 3D images (including dynamic images such        as video) can be used as both as a type of source data (e.g.,        requiring 3D-capable data capturing mechanisms) but also images        where the person/entity trying to access the data has to have        the correct devices to appropriately view/select the image. In        some cases for example access to images could only be        practically possible through viewing through 3D glasses—in        effect prohibiting bots or automated/computerized mechanisms        from being able to process such images.    -   Grid #15 extends the 3D concepts of Grid #14 even further by        portraying possible verification images in a dynamic, 3D manner,        such as a rotating Rubik's cube type presentation where images        are not only presented in 3D but also done in a temporary,        rotating manner that requires very fast decision making. This        concept is elaborated on further in FIGS. 13 and 14, where        solving Rubik's cube type images is required to verify an        account and/or gain access to the account/data and where        different portions of the cube are sources/controlled by        different users/sources involved in the data collection and/or        usage process.    -   Grid #16 illustrates the potential role and sourcing (and        security and privacy concerns) associated with smart homes and        the Internet of Things (IoT), as common household items such as        TVs evolve beyond being dumb or one-way communications        mechanisms to being able to collect, store, and transmit        location/context and other data about a user (TV viewer). The        image in Grid #16 is a possible example of a picture/video that        could be taken of a TV viewer by the TV itself. As shown also in        FIG. 1, IoT may play a major role in collecting        location/contextual data (particularly activity/behavioral data)        in places/situations not historically available to such data        collection. This type of household data has the potential for        being particularly sensitive, and as such needs the extra        protections offered by the location-based privacy and security        mechanisms, systems, and methods described herein.

To successfully use the protections and security described by thislocation-based privacy and security, there needs to be ways ofverifying, authorizing, or otherwise providing access to the very large(and often very sensitive) volumes of data collected. At its simplest, alocation-based key (or password) needs to be matched to the correctimage/answer in order to proceed further. There are numerous ways ofmatching two images to see if they are the same. Many have to do withthe degree of uniqueness, e.g., do the two images share the same unusualrepresentation or pattern—sometimes done on a pixel-by-pixel basis.

For exemplary embodiments of the present disclosure, doing exact matchesare fairly straightforward, as the images being offered as averification/password matching option may often be the exact imagestored in a reference database, with few if any technical differences.Where degree of uniqueness comes into play is when certainlocation/contextual elements are the focus of the verification questionor password sequence, such as requesting all images that showchildren-at-play in my backyard during summer of 20XX. In those cases,the possible images not only may not be an exact duplicate of abaseline/reference image, but there may also not be a baseline/referenceimage, and/or the possible correct images may seem very different to thetypical viewer. In these cases, it can be subset(s) of the images thatwould be important—those portions of the image that determine keylocation/context elements, such as “children”, “at play” and “summer2000.” In those cases, a person may designate the important elements, ora matching algorithm may pick out key elements, such as the presence ofa swing set to be the proxy for “play”, “children” being anyone under 5feet tall (this needing a reference height), or “Summer 2000” showingtrees and grass in full bloom, with a date stamp of June, July, August,or September 2000 as part of the image metadata. A statisticalprobability technique may also apply, such that any match with more thana 90% probability would be considered sufficient.

A new way of dealing with the variances/uncertainties ofmatching/verifying images that are not technically/digitally the sameyet have sufficiently matching elements is location/contextfingerprinting, described in the present disclosure. As background, inthe wireless field, there is a concept called Radio Frequency (RF)Fingerprinting. This is a generally a location-determination methodwhere surrounding cellular or Wi-Fi signals at a given point aremeasured (such as measuring signal strength, or time-differences), andthose measurements stamped with a GPS location—constituting afingerprint of that location. When a user of such a system later reportsa variety of signal measurements, they can be compared to thefingerprint database, and if they match a fingerprint in the database,then that user is reported to be at the corresponding location.

A new variant of that concept is disclosed here. For example, averification database may have many hundreds or thousands of images of alocation and/or context—some drawn from/known to a user and others not.In a verification process, the verification engine could offer severalimages, and request the user to select the 3 that all have the samelocation or context in common. The common element could be any number ofthings—same location (home) or context (playing with my children), samelocation at the same point in time (summer last year), etc. It couldeven be which images were taken from the same (user) device—e.g.,matching based on source device and/or metadata attached to the imageversus the image itself. All of these examples would be relatively easyfor the user to remember and remember quickly; a hacker would have adifficult, even impossible job in deducing the correct answers. Like aphysical fingerprint is only on the user's physical person, theselocation/context fingerprints are available only in the user's brain. Asimple example of a location-based fingerprint is to have a front, side,and top image of the same object or situation that the user then has torecognize. For example, while such images of a house are readilyavailable from various sources (e.g., Google Earth, Zillow), suchdifferent perspectives of the same object would look very different tosomeone without a vested interest in the property (e.g., the owner), andthus it would likely only be an owner/resident that could quickly pickout 3 such images from a collection of several, for example.

The above is another instance of the importance and effectiveness oflocation/context-based privacy and security in protecting against robotsor bots seeking to compromise data/system security. For example, whenasked to verify whether or not a person is a robot, many such existingtechniques display several photos and ask the user to select those thatdisplay portions of street signs or store fronts. If the user correctlyselects all frames, then they are allowed to proceed with thetransaction. As an enhancement, the present disclosure could provideimages of locations personal to the user. For example, a variety ofstorefront images could be presented to the user, and the user asked toselect which ones they have been to within, say, the last 24 hours.These transactions could be selected according to a procedure,algorithm, or even randomly from the locations collected from the userduring that time period. This kind of verification would deter asophisticated robot as the validation process would be based on theuser's personal experiences and not a robot's general image recognitioncapability. Similarly, several street signs with a real or systemicallyoverlaid image of a street or road could be displayed and ask the userto select which streets/roads the user has traveled in the last week.The system could provide as much granularity as needed in selecting theroads, whereas a “week” could be very specifically 7 days, or generallyseveral days, depending on how good the user's memory is (and which isdescribed in their memory profile discussed shortly).

Such a query could be structured to include locations/streets/roads thatthe system knows definitively has not been to during the proscribed timeperiod. This could create by a simple geo-fence-type algorithm thatencapsulates a user's movements in a particularly geographicaldesignation, such as town, city, zip code, county, or state for example,within a certain time period, and then providing other selection optionsclearly outside those areas during that time. A simple illustrationcould ask the person “have you been to O'Fallon, Illinois recently?” Thesystem's data store would know if the user has ever been there, but suchanswer is not part of public record such as asking where you've lived inthe past or even the model of car that you have owned. The verificationis based on the user's personally experiencedlocation/context—information unlikely to be available in other databasesthat are accessible by hackers.

The selection of the location transaction to base a query on could bedone in a variety of ways. The selection of the preferred method couldbe established beforehand in user profiles, for example, giving the userchoices to select locations for verification purposes based on day(e.g., Saturday), time (only in the afternoon), time period (within thelast day, week, or month), historical only (only last year's locations),geography (only Missouri locations), and/or context (e.g., locationswhen I had been on vacation and/or clearly engaged in leisureactivities). This allows the user to be prompted with locations thatthey are most likely to remember, yet with little or no obviousness to ahacker.

Indeed, the above could be used not just for verification or resetpurposes, but as an application/system password or key itself. Everytime a person logs onto Application B for example, instead of beingasked for an alphanumeric-based password, it could be shown as severalimages—individual or parts of images, e.g., as shown in the jigsaw orRubik's cube puzzles in FIGS. 11 and 13. Images are not limited solelyto visual/graphical items. The images may be visual, audio,alphanumeric, or other types of passwords depending on the userinterface but are nonetheless referred to as images for convenience. Inwhatever form, the images are location and/or context-based and/orpersonally experienced by or known to the user. Several images could bedisplayed to the user, and the user may be asked to select the one(s)they've experienced at or during a selected time period. Or a timeperiod could be displayed with images all experienced or known to theuser, then the user may be asked to select the correct time-period froma list of options. An incorrect selection would be replaced by newoptions with a new answer that is based on the user's location/contextexperience or knowledge.

The user interface used in the verification/password selection processcan do more besides providing a large variety of image and image-typeselections—it can also enable new types of verification methods. Forexample, a 3D touch-type screen could enable choosing the correct imagesas they are raised or elevated in the view screen, and the user cantouch/press those images that are correct/in the correct sequence, in akind of virtual whack-a-mole mechanism. This concept is illustrated inFIG. 14. An example embodiment of this concept is that the user ispresented with a static or rotating image (or cube) that has one or morepictures being enhanced (e.g., popped-up, etc.) every few seconds. Theperson trying to gain access to the data/account/system would only have,for example, 2-3 seconds to select (virtually whack) the validoption(s)—validity being dependent on the verification/authorizationquestion or statement, such as “select all images showing your kidsplaying in your backyard in summer 20XX.” This is an example of aquestion that would be relatively easy for a valid user to answerbecause the user could readily recognize the user's own children, ownbackyard, and even season/year (if, for example, the user's backyard wasa landscaping mess up to Spring of that year, and a new swing setreplaced the old swing set the winter of 20XX). Conversely, this wouldbe very difficult if not impossible for a bot or other hacking-typealgorithm to discern the correct answer(s).

To provide further protection, the location data used could be selectedrandomly from the historical data store both in time and/or place. Alive, real-time, or near-live/real-time (e.g., only a few seconds,minutes, hours-old, etc.) stream or recording could also be used,requiring the user to relatively instantaneously or instantly recognizeimages not seen or recorded anywhere else because they are happening nowor just happened.

In exemplary embodiments associated with addiction, there are additionallocation-based security embodiments above and beyond (or particularlytailored) to addiction-related issues and/or data sensitivity. Forexample, one or more specific persons may be provided with control of alocation-key that is based on some personal information of the addictand/or support person(s). This key could be relatively static, changingrelatively infrequently, or dynamic, changing perhaps every day or evenhour. The premise is that much—even most—of an addict's data will gounused or used very infrequently—thus it is not necessary to have acommonly-known, even easily accessible password or equivalent. Becauseexemplary embodiments of the present disclosure are generally centeredaround the prevention of relapses/usage of substance or activity, ifduring a given period of time there has been no usage-related activity,there is little reason to retain that information once key addictlocation/context/behavior information (such as rewards-eligible behaviorcalculations) has been extracted. Once the data has been fully used, itcan then be erased in a Snapchat-like manner or archived with alocation-based password or key, or pointer to who has the password orkey. Or, portions of the data could be randomly selected (or selectedbased on the user's memory profile) and stored for futureverification/location-based security purposes. Depending on the addict,addict-situation, controller or controller-situation, or other factorssuch as court-orders or law enforcement requirements, knowledge ofand/or access to location/context-related keys could be bypassed (orenhanced) to make it easier (or more difficult) for an addict'slocation/context data to be readily accessed. In certain circumstances,the addict's consent could be an absolute requirement for anyone toaccess the data—in other circumstances (e.g., court orders) the addict'sconsent might not be required at all.

The duration or longevity of location/context-related data, particularlyaddiction-related data, is also significant. As indicated in the processflow in FIG. 12, not all location/context data would be storedindefinitely. To the contrary, once the utility of an addict'slocation/context data has been fully utilized—either in detecting and/orpreventing and/or dealing with a high risk/relapse situation, orlearning/adjusting/modifying applicable learning mechanisms, as well asrewards, there may not be any reason to (continue to) store the data andthus can be deleted in Snapchat-type fashion. On the other hand, ifthere is a longer-term reason to store such data (court orders, a desireby the addict to journal addiction recovery, etc.), thenlocation/context-based privacy and security measures would be put inplace.

If location/context data for an addict is indeed archived, in general itshould not be easy to retrieve and be extension very difficult fornon-authorized persons or programs to access. Location-based security,with different pieces of historical data protected by differentlocation-based security mechanisms and passwords known to differentpersons, would achieve this high level of protection. To resurrect a24-hour period for example may require location-based passwords fromseveral different people—an electronic version of old-style banklockboxes that require multiple keys to be inserted at the same time toopen the box. Instead, to retrieve the location data from February 18,20XX (for addict A, it may require location-based passwords from thethree different addict controllers that were on-duty that day, as wellas the addict themselves, to reassemble/reconstruct that day.

Images used in a visual matching scheme such as in FIG. 11 need not beones actually taken in photo form by a user or otherwise captured in agraphical form. For example, if the user had been detected at being atthe intersection of Santa Monica Blvd and Sophie Street within anacceptable time frame (according to the user's memory profile), a streetsign could be generated showing a street intersection sign image. Suchimages could even be generated using even more abstract mechanisms, suchas a mileage street sign showing how far to certain locations could bederived by a visited location, then displayed in visual form. Forexample, if a person was in Disney World in Orlando, Fla. 2 months ago,and their profile allows for vacation locations within the last year tobe used, a street sign could be shown that says 204 miles to Miami, 74miles to Tampa, etc.—those being the mileages between Orlando and thosecities. Thus, an image-based location key could be generated just usinga visited location or latitude/longitude.

The image selection process for the above embodiments can be simplisticor very difficult. On the simplistic end, for example, only one ofseveral images could be valid. On the difficult end, several could bevalid, but they must be selected in order of oldest to newest (or viceversa). The latter, for example, could be images (pictures, addresses,etc.) of previous home locations, pets (that were only alive at acertain property), etc. that only the user is likely to readily know inthe proper sequence, yet not require little additional effort by theuser to actually remember. Thus, it would be possible to create a verycomplex password or verification sequence, yet easy for the user tounderstand, and nearly impossible for an outside party to know, at leastwithout extensive research. A time or other context-based limit toprovide the answers could be included in the verification algorithm toprevent such research from successfully taking place.

Another exemplary embodiment or variation of the present disclosure uses3-dimensional, jigsaw-type verification. As seen in FIGS. 12, 13, and14, a location or context can be divided into numerous pieces, thenscrambled similar to a (2D or 3D) jigsaw puzzle, requiring the entirepuzzle to be solved, only certain pieces of it solved, or solved for aparticular type of solution, theme, and/or in a given time-sequence forexample. The general philosophy behind such puzzle approaches is that itbecomes progressively (even exponentially) more difficult for ahuman/entity not familiar with the locations/contexts involved to solvethe puzzle, while being only incrementally more difficult for thosehumans/entities who are familiar with the locations/contexts involved.

That said, it is possible to use location-based concepts in thegeneration of a traditional password. For example, if the first 9 imagesin FIG. 11 were offered as possible answers to a verificationquestion/statement, and there were only two correct images out of the 9,with each frame being assigned a value from 1 to 9, then a point scorecould be calculated based on the values assigned to each image. A simpleexample would be if the two correct images had a value of 3 and 7,respectively, the resulting key/password could be thirty-seven(concatenation of the values) or twenty-one (the multiple). Thattraditional password adding potential capitalization and/or numbers,such as “Thirtyseven37”, could then be used by the various addiction (orother verification) analytical systems in accessing active data.

The various exemplary embodiments described above may provide anextremely sophisticated capability that establishes and systemicallyenforces privacy policies to support a balance of functionality andprivacy. There are at least two main types of privacy policy scenariosthat may be established and enforced. The first is where the addictvoluntarily signs up for functionality as disclosed herein. For such ascenario and associated policy, many of the more severe elements of suchfunctionality may be made optional, such as disabling paymentmechanisms, etc. A second scenario is an involuntary sign up of anaddict by other parties with the legal right, such as by parents ofminors, via judicial judgments, etc. In such cases, the functionality tobe activated may be decided by those parties with or without theaddict's consent or even without their knowledge (if deemed legal).Various exemplary embodiments may support and, in some cases, requirethe coordination and integration of privacy and/or security policies andsystems by a host of parties: application(s) as disclosed herein;financial entities; support group entities (e.g., AA, etc.), publicsafety and law enforcement entities; education entities (e.g., forteens, etc.); retail/wholesale chains and individual stores, serviceareas (e.g., movie theaters, etc.) and services; individuals, and thelike.

Various exemplary embodiments may provide functionality specific to agiven demographic. One example of this is for teenagers. Teenagers, andespecially teenage addicts—regardless of their addiction (though usuallydrugs or alcohol)—can have triggers and influencers in their lives thatare particularly distinct from other demographic categories: high schoolangst, peer pressure, parental pressure, academic pressure, sexualityissues, not to mention that most of the mechanisms for enabling theaddiction to begin with are illegal. Also, many addicts in that agedemographic have not reached the conclusion that they are an addict, letalone think they need help. They also are much more likely to betechnically savvy, which can be a double-edged sword. On the one hand,they would likely be loath to give up/not use those devices/mechanismsthat various embodiments may utilize (e.g., teenagers in this day andage are almost tethered at the hip with their cell phone, with one ofthe most often used disciplinary methods used by parents is taking awaytheir cell phone privileges). On the other hand, teenagers are one of ifnot the most inventive demographic in getting around technical issuesand constraints on their activities. It is anticipated that teenagersmay represent a significant percentage of involuntary users of variousembodiments. Accordingly, in an example embodiment, social networks maybe used in detecting true friends (non-addicts or non-enablers) withthose who are technically friends in an addict's network but arerecognized by the addict, his family, and/or other entities (e.g.,judicial system, etc.) to be a negative influence on the addict. Forexample, permission may be given by these friends (particularly thenegative type) to transfer, copy, or otherwise apply that permission forthe addict to track the friend to the application of the exampleembodiment. Thus, the negative friend's location could be used in theexample embodiment to help the addict stay away from that so-calledfriend. This could be done with or without the addict's and/or thefriend's permission, e.g., supporting the voluntary and involuntaryprivacy scenarios described above.

Unlike smoking where addiction may occur relatively immediately,alcoholism generally takes much longer and is much more nuanced in itsprogression, hence alcoholics may have very long periods of denial. Inexemplary embodiments disclosed herein, sensors and other mechanisms maybe utilized for alcoholism testing for a person consciously or notand/or with or without the person's consent.

In various exemplary embodiments of the present disclosure, variousfunctionalities described herein may be integrated as a whole or inpart, e.g., into one or more methods, mechanisms, and/or applications.While any individual element above could be implemented individually, itis anticipated that much of the value of embodiments of the presentdisclosure is in the integration of the above, in whole or in part, toaccommodate the wide range of addiction enablers and alternatives, andthe various technology platforms that could be involved. Suchintegration may include other applications such as family finders,social networking applications, weather monitoring, navigationapplications, Facebook, Groupon, etc. Various embodiments can provideways in which to make the most of the moment of value, e.g., at timesand/or in context where an addict is in significant danger of relapsing.

That said, many features/aspects of the present disclosure are alsoanticipated to be of applicability to non-addicts or partiallyaddict-related scenarios, such as persons with common medicalconditions, sports enthusiasts, dating websites, law enforcement (e.g.,additional functionality/flexibility beyond just GPS bracelets,providing other flexibility to the judicial system such providinginnovative options to judges in DUI cases to revoke their parole if theyare found to have stopped at a liquor store, etc.), medicalapplications, insurance applications, employee verification, medicalalerts, amber alerts, suicide prevention, etc. For example, bloodalcohol sensors may indicate a relapse by an alcoholic. Any one or moretriggers as disclosed herein may also or instead be used to identify anincreasing suicide risk and be monitored accordingly.

All of the above covers one or more addictions; a substantial portion ofthe addict community has more than one addiction. Also, it coversaddictions that may be replaced by others, such as replacing alcoholwith caffeine and/or sugar.

Exemplary embodiments are disclosed of systems and methods of usinglocation, context, and/or one or more communication networks formonitoring for, preempting, and/or mitigating pre-identified behavior.For example, exemplary embodiments disclosed herein may includeinvoluntarily, automatically, and/or wirelessly monitoring/mitigatingundesirable behavior (e.g., addiction related undesirable behavior,etc.) of a person (e.g., an addict, a parolee, a user of a system,etc.). In an exemplary embodiment, a system generally includes aplurality of devices and/or sensors configured to determine, through oneor more communications networks, a location of a person and/or a contextof the person at the location; predict and evaluate a risk of apre-identified behavior by the person in relation to the location and/orthe context; and facilitate one or more actions and/or activities tomitigate the risk of the pre-identified behavior, if any, and/or reactto the pre-identified behavior, if any, by the person.

The pre-identified behavior may include pre-identified addiction-relatedundesirable behavior, and the system may be configured to be operablefor monitoring for, preempting, and/or mitigating the pre-identifiedaddiction-related undesirable behavior. The system may be configured todetermine, through the one or more communications networks, a locationof an addict and/or a context of the addict at the location; predict andevaluate a risk of relapse by the addict in relation to the locationand/or the context; and facilitate one or more actions and/or activitiesto mitigate the risk of relapse, if any, and/or react to the relapse, ifany, by the addict. The system may be configured to determine whetherone or more addiction triggers predetermined in the system are active orpresent based on the location and/or the context and/or biometric,environmental, and/or behavioral data for the person. The system may beconfigured to determine whether one or more addiction triggerspredetermined in the system are active or present by comparing data fromone or more of the plurality of devices and/or sensors with one or moresettings for the person. The one or more settings for the person mayinclude one or more of blood pressure, heart rate, skin temperature,body temperature, respiratory rate, perspiration, weight, exerciseschedule, external temperature, noise loudness, and/or noise frequency.The plurality of devices and/or sensors may comprise one or morebiometric, environmental, and/or behavioral sensors that provide thebiometric, environmental, and/or behavioral data for the person usableby the system in determining whether one or more addiction triggerspredetermined in the system are active or present. The system may beconfigured to receive and process feedback and to adjust the pluralityof devices and/or sensors including increasing, decreasing, and/orotherwise modifying one or more of the settings and/or a frequency ofdata collection in response to the feedback including actions andbehaviors of the person associated with the data.

The system may be configured to predict and evaluate a risk of thepre-identified behavior by the person in relation to the location and/orthe context by using data from one or more of the plurality of devicesand/or sensors. The one or more of the plurality of devices and/orsensors may comprise one or more biometric, environmental, and/orbehavioral sensors. The one or more of the plurality of devices and/orsensors may comprise one or more of a blood pressure sensor, abreathalyzer, a blood alcohol content sensor, a thermometer, a skintemperature sensor, a breathing rate sensor, a heart rate sensor, a skinmoisture sensor, an olfactory sensor, a vestibular sensor, a kinestheticsensor, an optical sensor, a retinal scanner, a voice recognitionsensor, a fingerprint sensor, a facial recognition sensor, abiogestation sensor, an acoustic sensor, a microphone, a weather sensor,a barometer, a precipitation sensor, a gyroscope, an accelerometer,and/or a compass.

The one or more communications networks comprise an Internet of Thingsnetwork including one or more physical devices, items, vehicles, homeappliances, and/or household items usable by the system for determiningthe location of the person and/or the context of the person at thelocation. The system may be configured to determine the location of theperson via one or more of an Internet of Things network, a globalpositioning system, cell tower identification, cell tower triangulation,a beacon, radio frequency fingerprinting, real-time location services,Wi-Fi based location systems, radio frequency identification basedlocation systems, a drone, crowdsourcing, and/or simultaneouslocalization and mapping.

The system may be configured to predict and evaluate a risk of thepre-identified behavior by the person by using the location and/or thecontext and one or more of biometric, environmental, and/or behavioraldata of the person; voice data of the person and/or another person,including one or more of tone, inflection, cadence, tempo, and/orpre-identified words; and/or movement data, including the person'swalking gate, stride, and/or direction of travel; and/or date and/ortime of day; and/or historical visitation patterns of the person topredict and evaluate a risk of the pre-identified behavior by theperson; and/or monitoring social media.

The system may be configured to detect and track behavior of the personvia the plurality of devices and/or sensors to determine applicabilityand value of behavior and to provide a corresponding incentive ordisincentive for the person. The system may be configured to facilitateavoidance of one or more predetermined locations by omitting the one ormore predetermined locations from one or more navigation applicationsand/or by de-augmenting the one or more predetermined locations from oneor more augmented reality applications. The system may be configured toestablish one or more geo-fences for one or more predetermined locationsand to provide one or more alerts when the person crosses a geo-fence toenter or exit the predetermined location corresponding to the geo-fenceor otherwise violates the parameters associated with the geo-fence. Thesystem may be configured to use the plurality of devices and/or sensorsto assess a likelihood that the person is an alcoholic, drug addict,activity addict, and/or substance abuser.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network. The location of the personmay be a physical location or a virtual location. The context mayinclude a situation, an environment, and/or a state of mind of theperson based on one or more of biometric, environmental, and/orbehavioral data of the person.

The plurality of devices and/or sensors may include a plurality ofsensors configured to monitor the location and/or the context of theperson at the location. One or more of the plurality of sensors may belocated in, on, and/or near the person. A plurality of interface devicesmay be configured to engage in interaction with the person, with one ormore support persons for the person, and/or with one or more thirdparties in the event the system determines a relationship between thelocation and/or the context and one or more triggers predetermined inthe system that indicates a risk of the pre-identified behavior by theperson. The system may be configured to select the interaction based onthe one or more triggers and the location and/or the context of theperson at the location.

The system may be configured to develop and/or update a profile of theperson including one or more predetermined actions to implement for theperson depending on the prediction and evaluation of the risk of thepre-identified behavior by the person in relation to the location and/orthe context. The person may be a parolee, and the one or morecommunications networks allow the system to monitor the location of theperson both indoors and outdoors. The system may be configured to beusable by another one or more persons to voluntarily and/orinvoluntarily monitor the location of the person and/or the context ofthe person at the location.

The one or more actions and/or activities facilitated by the system mayinclude one or more of requesting the person to attend a nearbyaddiction support meeting, visit another one or more persons in asupport network, and/or travel to a predetermined location for a certainactivity; and/or providing an alert to a family member, medicalpersonnel, law enforcement, or other support person or persons; and/ordisabling a vehicle of the person; and/or automatically changingoperation of a vehicle of the person to driverless; and/or informing acommunity member or addiction sponsor of the person; and/or monitoringthe location of the person and a location of one or more support networkpersons and determining one or more scenarios that allow one or moresupport persons to be dispatched to the person's location or vice versa;and/or automatically playing a voice of a family member or a friend;and/or linking and coordinating an ad hoc meeting between the person andanother person, persons, or group; and/or providing a location-basedalternative and/or a location-based advertisement to the person via amobile phone; and/or provide linkages to a mobile phone that provide oneor more personal and/or impersonal reminders to the person aboutaddiction consequences.

The system may be configured to restrict and condition access to thesystem and/or to the person's data collected by one or more of theplurality of devices and/or sensors through the one or morecommunications networks based on selection of location-based data forthe person from a plurality of options presented by the system forselection, the plurality of options including the location-based dataand one or more other options. The system may be configured to use oneor more interface devices for interfacing with the person and/or todisseminate information to/from the person and/or one or more supportpersons for the person. The one or more interface devices may compriseone or more of tangible and/or tactile interfaces including one or moreof a display, illumination, sound, vibration, heat, and/or smellinterface.

The system may be configured to detect a relationship between thelocation and/or the context and one or more triggers predetermined forthe person as being related to the pre-identified behavior; and based onthe detected relationship, use one or more interface devices,mechanisms, or techniques to interact with the person, with one or moresupport persons for the person, and/or with a third party.

In an exemplary embodiment, a method for monitoring for, preempting,and/or mitigating pre-identified behavior generally includesdetermining, via one or more devices and/or sensors across one or morecommunications networks, a location of a person and/or a context of theperson at the location; predicting and evaluating a risk of apre-identified behavior by the person in relation to the location and/orthe context; and facilitating one or more actions and/or activities tomitigate the risk of the pre-identified behavior, if any, and/or reactto the pre-identified behavior, if any, by the person.

The method may include detecting a relationship between the locationand/or the context and one or more triggers predetermined for the personas being related to the pre-identified behavior; and based on thedetected relationship, using one or more interface devices, mechanisms,or techniques to interact with the person, with one or more supportpersons for the person, and/or with a third party.

The method may include determining whether the location and/or thecontext correspond to a high-risk location and context, then identifyingone or more potential actions and/or available support resources tomitigate the risk of the pre-identified behavior, selecting one or moreactions and one or more interfaces for the person, and implementing theselected action(s) and interface(s) for the person; and/or selecting andimplementing one or more actions and one or more interfaces for theperson if the location and/or the context indicate an immediate highrisk of the pre-identified behavior; and/or selecting and implementingone or more preventive actions for the person if the location and/or thecontext correspond to a trending risk of the pre-identified behavior orbehaviors, and/or adjusting and continuing to monitor the person'slocation and context at the location.

The method may include determining, projecting, or predicting a currentor future context of the person at the location by analyzing and linkingreal-time data and historical data for the person, the real-time andhistorical data including the location of the person, data from the oneor more devices and/or sensors, historical context of the person at thelocation, behavior patterns, travel patterns, health data, and riskcalculations; and/or monitoring the person's physical and mentalcondition via the one or more devices and/or sensors including one ormore wearable sensors and/or embedded sensors.

Facilitating one or more actions and/or activities may comprisedetermining which one or more devices and/or sensors are in use;determining available interfaces on the one or more devices and/orsensors that are determined to be in use; determining an inventory ofpotential interfaces desired by selected actions and that satisfy aprivacy requirement and/or live 2-way communication requirement; andselecting and implementing one or more interfaces from the inventory ofpotential interfaces.

The method may include determining, through the one or morecommunications networks, a location of an addict and/or a context of theaddict at the location; predicting and evaluating a risk of relapse bythe addict in relation to the location and/or the context; andfacilitating one or more actions and/or activities to mitigate the riskof relapse, if any, and/or react to the relapse, if any, by the addict.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network. The location of the personmay be a physical location or a virtual location. The determination ofthe context may be based on one or more of biometric, environmental,and/or behavioral data of the person. The pre-identified behavior mayinclude pre-identified addiction-related undesirable behavior. Themethod may include monitoring for, preempting, and/or mitigating thepre-identified addiction-related undesirable behavior. The method mayinclude determining whether one or more addiction triggers are active orpresent based on the location and/or the context and/or biometric,environmental, and/or behavioral data for the person.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprises computer-executable instructions for monitoring for,preempting, and/or mitigating pre-identified behavior, which whenexecuted by at least one processor, cause the at least one processor to:determine, via one or more devices and/or sensors across one or morecommunications networks, a location of a person and/or a context of theperson at the location; predict and evaluate a risk of a pre-identifiedbehavior by the person in relation to the location and/or the context;and facilitate one or more actions and/or activities to mitigate therisk of the pre-identified behavior, if any, and/or react to thepre-identified behavior, if any, by the person.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, and/orthe Internet of Things. The location of the person may be a physicallocation or a virtual location. The determination of the context may bebased on one or more of biometric, environmental, and/or behavioral dataof the person. The pre-identified behavior may include pre-identifiedaddiction-related undesirable behavior.

Also disclosed are exemplary embodiments of systems and methods forproviding location-based security and privacy for restricting useraccess. In an exemplary embodiment, a system is configured to restrictand condition access to the system and/or data based on a user'sselection of location-based data from a plurality of options presentedby the system for selection by the user. The plurality of optionsincludes the location-based data and one or more other options that areselectable by the user.

The system may be configured to present one or more queries and/orqualifiers to prompt the user to select corresponding location-baseddata from the plurality of options in response to the one or morequeries and/or qualifiers. The system may be configured to restrict theuser's access to the system and/or data at least until the correspondinglocation-based data is selected that satisfies the one or more queriesand/or qualifiers. The location-based data may comprise one or moreimages that satisfy the one or more queries and/or qualifiers. The oneor more other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured to restrict the user's access to the system and/or data atleast until the corresponding one or more images are selected thatsatisfy the one or more queries and/or qualifiers, or when the one ormore other images are selected that do not satisfy the one or morequeries and/or qualifiers. The system may be configured so as to notrestrict the user's access to the system and/or data when thecorresponding one or more images are selected that satisfy the one ormore queries and/or qualifiers.

The location-based data may include a location of the user and/or acontext of the user at the location as determined by the system usingone or more of a plurality of user devices and/or sensors across one ormore communications networks. The location-based data may comprise dataobtained by the system via an Internet of Things network of physicaldevices, items, vehicles, home appliances, and/or household items usableby the system for determining a location of the user and/or a context ofthe user at the location. The location-based data may comprise one ormore images based on a location and/or a context of the location toand/or known by the user, whereby the one or more images are usable bythe system as one or more passwords or keys for permitting access to theuser data.

The plurality of options may comprise a plurality of images presented bythe system for selection by the user. The location-based data compriseone or more images based on a location and/or a context of the locationto and/or known by the user. The images may comprise one or more of avisual, audio, graphical, video-based, photographic, textual, and/oralphanumeric image; a sensor reading; a static image; a dynamic image; amultidimensional image; a past image; a present image; a future image; alive streaming image; an image of a vacation destination; an image of afamily member; an image of a pet; an image of a vehicle; an image of aresidence; an image of a location; and/or a virtual or augmented realityimage; and/or; a drawing; a distorted image; a modified image; and/or anartificially rendered image.

The system may be configured to present a multidimensional (e.g., 2D,3D, 4D, etc.) combination puzzle that includes one or more keys and thatis successfully completed when corresponding location-based data isselected from the plurality of options for the one or more keys. Thesystem may be configured to restrict the user's access to the systemand/or data at least until the successful completion of themultidimensional combination puzzle. The system may be configured topresent one or more queries and/or qualifiers to prompt the user toselect, for the one or more keys, the corresponding location-based datafrom the plurality of options in response to the one or more queriesand/or qualifiers. The system may be configured such that themultidimensional combination puzzle is successfully completed when thecorresponding location-based data is selected for the one or more keysthat satisfy the one or more queries and/or qualifiers. Thelocation-based data may comprise one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers. The one ormore other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured such that the multidimensional combination puzzle issuccessfully completed when the corresponding one or more images areselected for the one or more keys that satisfy the one or more queriesand/or qualifiers. The system may be configured such that themultidimensional combination puzzle comprises a three-dimensional cubethat is successfully completed when the corresponding one or more imagesare selected for the one or more keys for at least one or more faces ofthe three-dimensional cube. The system may be configured to present theplurality of options to the user for selection as the one or more keysin a rotating, moving, and/or changing manner (e.g., zooming in/out,distorted, etc.) and/or for a predetermined amount of time. The systemmay be configured such that the multidimensional combination puzzlecomprises a two-dimensional grid or a jigsaw puzzle that is successfullycompleted when the corresponding location-based data is selected fromthe plurality of options for the one or more keys in a predeterminedorder or sequence.

The system may be configured to present one or more queries and/orqualifiers to prompt the user to select corresponding location-baseddata from the plurality of options in response to the one or morequeries and/or qualifiers. The location-based data may comprise one ormore images that are based on a location and/or a context of thelocation to and/or known by the user and that satisfy the one or morequeries and/or qualifiers. The one or more other options may compriseone or more other images that do not satisfy the one or more queriesand/or qualifiers. The system may be configured to restrict the user'saccess to the system and/or data at least until the selection of thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers and the corresponding one or more devices used tocapture the corresponding one or more images.

The location-based data may comprise a plurality of different images ofa location and/or context of the location to and/or known by the user.The one or more other options may comprise one or more other images. Thesystem may be configured to present one or more queries and/orqualifiers to prompt the user to select the corresponding images of thelocation and/or context in response to the one or more queries and/orqualifiers. The system may be configured to restrict the user's accessto the system and/or data at least until the corresponding images of thelocation and/or context are selected that satisfy the one or morequeries and/or qualifiers. The system may be configured to present oneor more queries and/or qualifiers to prompt the user to selectcorresponding location-based data from the plurality of options inresponse to the one or more queries and/or qualifiers. Thelocation-based data may comprise one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers. The one ormore other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured to assign a numerical value to the one or more images and tothe one or more other images. The system may be configured to use thenumerical value(s) of the corresponding one or more images selected bythe user that satisfy the one or more queries and/or qualifiers forgenerating a key or password for accessing the user data.

The location-based data may include recent and/or historical locationand/or context information of the user. The system may be configured touse the recent and/or historical location and/or context information ofthe user for a password reset verification, to prove that a user is nota robot, and/or to verify a financial transaction.

The system may be configured to restrict and condition access tobiometric data, environmental data, behavioral data, and/orlocation-based data for a person, obtained by the system via one or moreof a plurality of devices and/or sensors through one or morecommunications networks, based on the user's selection of location-baseddata for the person from the plurality of options presented by thesystem for selection by the user. The user may be the person, anotherperson, and/or an accessor.

The system may include a plurality of devices and/or sensors configuredto determine, through one or more communications networks, a location ofa person and/or a context of the person at the location; predict andevaluate a risk of a pre-identified behavior by the person in relationto the location and/or the context; and facilitate one or more actionsand/or activities to mitigate the risk of the pre-identified behavior,if any, and/or react to the pre-identified behavior, if any, by theperson. The system may be configured to restrict and condition access todata for the person, obtained via one or more of the plurality ofdevices and/or sensors through the one or more communications networks,based on a user's selection of location-based data for the person fromthe plurality of options presented by the system for selection by theuser, whereby the user is the person, another person, and/or anaccessor.

The system may be configured to restrict and condition access to aperson's data based on the user's selection of location-based data forthe person from the plurality of options presented by the system forselection by the user. The user may be the person, another person,and/or an accessor. The system may comprise a non-transitorycomputer-readable storage media including computer-executableinstructions, which when executed by at least one processor, cause theat least one processor to present the plurality of options for selectionby the user including the location-based data and the one or more otheroptions; determine whether the user selected the location-based datafrom the plurality of options; and restrict access to the system and/ordata at least until it is has been determined that the user selected thelocation-based data from the plurality of options.

In another exemplary embodiment, a method for providing security and/orprivacy generally includes presenting a plurality of options forselection by a user, the plurality of options including location-baseddata and one or more other options; determining whether the userselected the location-based data from the plurality of options; andrestricting the user's access to a system and/or data at least until itis has been determined that the user selected the location-based datafrom the plurality of options.

The method may include presenting one or more queries and/or qualifiersto prompt the user to select corresponding location-based data from theplurality of options in response to the one or more queries and/orqualifiers; determining whether the user selected the correspondinglocation-based data that satisfies the one or more queries and/orqualifiers; and restricting the user's access to the system and/or dataat least until it has been determined that the user selected thecorresponding location-based data that satisfies the one or more queriesand/or qualifiers.

The method may include presenting one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers; presentingone or more other images that do not satisfy the one or more queriesand/or qualifiers; and determining whether the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers; restricting the user's access to the system and/ordata at least until it has been determined that the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprises computer-executable instructions for providing securityand/or privacy, which when executed by at least one processor, cause theat least one processor to restrict and condition access to a systemand/or data based on a user's selection of location-based data from aplurality of options presented for selection by the user, the pluralityof options including the location-based data and one or more otheroptions that are selectable by the user.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present the pluralityof options for selection by the user including the location-based dataand the one or more other options; determine whether the user selectedthe location-based data from the plurality of options; and restrict theuser's access to the system and/or data at least until it is has beendetermined that the user selected the location-based data from theplurality of options.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present one or morequeries and/or qualifiers to prompt the user to select correspondinglocation-based data from the plurality of options in response to the oneor more queries and/or qualifiers; determine whether the user selectedthe corresponding location-based data that satisfies the one or morequeries and/or qualifiers; and restrict the user's access to the systemand/or data at least until it has been determined that the user selectedthe corresponding location-based data that satisfies the one or morequeries and/or qualifiers.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present one or moreimages that are based on a location and/or a context of the location toand/or known by the user and that satisfy the one or more queries and/orqualifiers; present one or more other images that do not satisfy the oneor more queries and/or qualifiers; determine whether the correspondingone or more images are selected that satisfy the one or more queriesand/or qualifiers; and restrict the user's access to the system and/ordata at least until it has been determined that the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers.

Exemplary embodiments are disclosed of systems and methods formonitoring for and preempting pre-identified restrictionviolation-related behavior(s) of persons under restriction. For example,disclosed are exemplary embodiments of systems and methods of preemptingand modifying behavior via sensors and the Internet of Things (IoT) toexpand and enhance sentencing, probation, and/or parolee monitoring,enforcement options, and capabilities.

Exemplary embodiments of the present disclosure include systems andmethods for monitoring, tracking, obtaining/collecting, and analyzinglocation, context, behavior/behavior patterns, and/or behavior andcontext-based trigger information about parolee(s), prisoner(s), orperson(s) on probation (“parolee”) using sensors and/or sensor networkson, in and/or around one or more parolees and one or more communicationsnetworks connected to and/or associated with the parolee(s). The systemsand methods may include providing information/feedback and support tothose parolees or other persons and/or applications to help in themodification of the parolee's location, context, and/orbehavior/triggers before a violation occurs, or, if need be,preemptively responding to violations about to occur or in progress withactions geared towards providing information about locations, contexts,and/or behaviors to the appropriate authorities or other interestedpersons to help in their recapture and overall limiting of damage to thepublic and themselves.

Exemplary embodiments disclosed herein may enable new ways of sentencingor assigning parole or probation conditions (“sentencing options”),which are otherwise not possible or available today. Examples includesentencing DUI or DWI offenders to no drinking of alcohol or even beingnear alcohol or near those who have been drinking; prohibiting driving avehicle under certain conditions such as being angry, being in traffic,or having drank alcohol; prohibiting drug offenders from being nearhigh-risk crime (drug dealing) areas as well as drug-dealing “friends”;or identifying when a parolee (such as a sex offender) uses theInternet, watches inappropriate programming, or plays (e.g., violent,etc.) games. There is no limit to the types of sentences, orparole/probation conditions that could be enabled that are not possibleor available today. Some examples, while unusual, are indicative of newpossibilities that could have a major and/or positive impact on theparolee depending on the crime/violation involved. Examples includeprohibiting/restricting participation in certain hobbies; not allowingdrinking and/or smoking; performing public service activities with veryspecific times/permissible activities and/or with particular people; notbeing allowed to get angry or frustrated under any conditions or undercertain conditions, places, and/or times; being required to attend andparticipate in AA (alcoholics anonymous) meetings andconfirming/verifying such attendance and participation via exemplaryembodiments of the present disclosure not just relying upon paperworksigned for the AA meeting; getting at least eight hours of sleep a nightand never watching anything rated more violent than PG; interactingpersonally with certain people at least once a day; not playing video(e.g., Xbox, PS4, etc.) games with certain persons at certain times ofday over certain amounts of time; requiring a clean residence at alltimes; not viewing (in any form) any sports events; eating at least twobalanced meals a day; not getting excited/angry/frustrated whileperforming a job; not listening to certain kinds of music (e.g.,sexually violent rap music, etc.); not engaging in any sort of activitythat could be deemed sexually harassing; never yelling at your spouse,ex-spouse, and/or children, and so forth.

Beyond these types of new sentencing options, exemplary embodimentsdisclosed herein may enable new ways of monitoring, tracking, analyzing,interpreting, and acting upon behavior and context data (“trigger data”)from and/or about those monitored person(s). It may include tailoringsensor and other data collection mechanisms specifically towards aparolee's trigger history and profile (and predicated to some degree onthe violations/crimes involved) including key vulnerabilities or otheraspects that contributed to past crimes or may contribute to violationsof sentencing in the future—and new ways of analyzing that data, as wellas new ways of providing information/feedback about the monitoredperson(s) to third parties—not just to law enforcement-related such asparole officers, but also to support persons that can assist theparolees/prisoners, as well as to parolee/prisoners themselves to helpthem “self-correct” their behavior preemptively, before it reachesviolation status.

Particularly important is monitoring/measuring/calculating keyactivity/behavior “triggers” that might cause a violation. Triggers canbe defined as “situations, circumstances, activities, events, contexts,mental thought processes and/or frame-of-minds that tempt or cause aparolee to want to engage in an activity or behavior known or identifiedto be detrimental to the parolee and/or others.”

Such behaviors and triggers that could potentially bemonitored/measured/preempted/modified/subject to violation include angerlevels (e.g., relevant to general anger-based crimes, etc.),anxiety/pain-related issues (e.g., drug usage/theft, etc.),frustration-related specific issues (e.g., road range, etc.), excitementlevels (e.g., thrill seekers, etc.), hunger levels (e.g., theft, etc.),money issues (e.g., theft, drug distribution, etc.), associates/friends(e.g., peer pressure-related crimes, etc.), relative issues (e.g.,assault etc.), sexual activity (e.g., sexual crimes, etc.). Indeed, tothe extent that a parolee/prisoner argued in his/her trial(s) that a“certain thing” caused the crime to be committed, exemplary embodimentsdisclosed herein may thus enable monitoring (e.g., by the court, etc.)for that “certain thing” (e.g., behavior, activity, or even mindset orframe of mind, etc.) not possible or available today.

The feedback and information provided (e.g., real-time, etc.) to thirdparties such as support persons include descriptions of key locations,contexts, behaviors, incidents, and/or events such as parole violationsor violations “in process”, including historical information about suchbehaviors/triggers. More importantly, exemplary embodiments disclosedherein may provide pre-identified recommendations for, enablement of,and/or automatic execution of pre-identified actions and/or activitiesthat would preemptively mitigate/change his/her location, context,and/or behavior before it reaches violation status. The feedback to theparolees, support persons, or law enforcement could include warningsabout “in process” violations, disturbing trends, or other aspects ofthe monitored person(s) location, context, and/or behavior that have thepotential of being reversed or corrected before it reaches violationstatus, along with specific actions based on the parolee's past orpresent behavior or anticipated future behavior.

In total, exemplary embodiments disclosed herein enable new sentencingoptions; preemptive tailored/personalized monitoring of behavior,context, and triggers; use of support resources; knowledge of aparolee's triggers and those actions that may preempt/prevent/mitigatenegative triggers, and the developing of actions based on all of theabove to prevent/mitigate recidivism/relapse of the parolee wouldprovide great benefits to the public and to theparolees/offenders/violators themselves. In the unfortunate case thatsuch violations do occur, exemplary embodiments disclosed herein mayalso provide new ways of preemptively aiding in apprehension of theviolator and in general preventing/mitigating any additional damage tothe public.

Previous inventions in the parolee/prisoner tracking field have focusedon GPS-based centralized tracking, monitoring, and detection oflocation-based violations, such as violating home detention or being inproximity to forbidden areas such as schools, spouse, or businesses. Thereliance on GPS, such as in a GPS ankle bracelet, which in itself issusceptible to tampering, in such prior inventions has been inherentlylimited, as GPS generally does not work well indoors, and even outdoorsit is susceptible to a variety of factors that can result ininaccuracies of 50 meters or more, limiting applications and use casesand even potentially resulting in erroneous alarms. The reliance oncentralized monitoring of the monitored person has also caused a varietyof issues (e.g., if the parole officer is on vacation with no backup,etc.), as has reliance on tamper-detection capability on a GPS anklebracelet which can provide no benefits if the monitored person decideshe/she doesn't care anymore and decides to go on a non-tracked crimespree.

The focus on the location of a monitored person has resulted in limitedattention being given to the least tangible aspects of the monitoredperson's life, particularly their mindset/frame-of-mind (such as beingable to monitor their anger levels), as well as their broader activitiesand behavior while they are inside their home or other “confined” area(where GPS doesn't work). To the degree that such activities/behaviorhas been focused on, it has been for the hastening/accelerating ofpotential violations—not for purposes of preempting such a violation, oreven proactively enlisting a support network to modify theparolee's/prisoner's behavior before it becomes a real or actualviolation-worthy problem. Finally, the security of parolee/prisonerinformation has generally been assumed to be secure and/or of littleinterest to anyone outside law enforcement; such an assumption today andgoing forward will likely be incorrect; in the future “stewards” of aparolee's data could be others such as support personnel keenlyinterested in preventing the parolee from having his sentence invoked orparole/probation revoked.

After recognizing the above issues, drawbacks, and obstacles withparolee monitoring and tracking, exemplary embodiments were developedand/or are disclosed herein that may address all of the above-notedparolee monitoring and tracking issues, drawbacks, or obstacles. Asdisclosed herein, exemplary embodiments may provide more comprehensive,more accurate, more preemptive, and more supportive monitoring andtracking, through one or more of the following elements:

-   -   Deploying and monitoring indoor and wearable sensors connected        via an Internet-of-Things (IoT) network.    -   Use of the IoT network to decentralize or otherwise monitor        “at-the-edge” potential rule breaking and other concerns.    -   Leveraging of other networks and environments (inside and        outside, even ones without electronic or physical access by the        parolee), for the purpose of behavior/activity assessment and        analysis.    -   Connection to other sensor-based networks to provide broader,        seamless, 24/7, anywhere/any time monitoring to prevent a        violation at any time, in any circumstance.    -   Using pre-identified prevention/preempting behavior and        context-based “triggers” to appropriate select and calibrate an        appropriate subset of sensors (from a wide range of sensors,        across all networks and environments), targeted at measuring and        calculating/analyzing current behavior versus permitted        “pre-identified” behavior and context, and, as needed relative        to historical behavior and context. Examples of triggers include        but are not limited to: Anger, Anxiety, Boredom, Change,        Children, Conflict, Depression, Disorder, Embarrassment, Escape,        Envy, Excitement, Fun, Frustration, Guilt, Health issues,        Holidays Hunger, Insomnia, Job stress, Loneliness, Mid-life        Crises, Money worries, Noise, Overconfidence, Pain, Peer        Pressure, feeling Powerful or Powerless, Proximity (e.g., to the        substance, etc.), Fear of Quitting (e.g., the substance or        activity, etc.), Relationship issues, Relatives, Reminders, Sex,        Shopping Situations, Social Situations, Special Occasions,        Stress, Taste and Smell, Times of Day, being Tired, being        “Unfun”, being a Victim (e.g., of crime, abuse, etc.),        ex-spouses/partners, Yelling, even Season or Weather changes,        and/or Music.    -   This term “context” as used herein may refer to the situation or        circumstances in which a behavior, event, or activity occurs,        e.g., the particular setting in which the        behavior/event/activity occurs, etc. For example, when        attempting to understand behavior, it is important to look at        the situation or circumstances present at the time of the        behavior. For example, the behavior Anger can be detected        through measuring blood pressure, heart rate, skin temperature,        and detection of yelling sounds, and for some people be        considered a trigger by itself. But for some other people, in        some circumstances, it is particularly valuable to know the        context. For example, a person might be in the presence of his        ex-spouse, and they may be in an argument. Or, for example, they        could be having the argument in a public place on a hot day with        no air conditioning. Having this understanding of context, in        addition to behavior, helps to select the best preemptive or        mitigating actions to cool down the Anger before a violation        (e.g., a drinking relapse, etc.) occurs, such as getting the        parolee physically away from the ex-spouse as soon as possible        and to go cool down in an air conditioned, private place.    -   While there are potentially hundreds of possible addiction        triggers (in the form of behaviors, contexts, or both behavior        and context combinations), and many thousands of trigger        combinations (such as Anger and Loneliness occurring at the same        time), exemplary embodiments disclosed herein may emphasize the        “pre-identification” of one or more of these behaviors and/or        context-based triggers as a way to select, calibrate, and        analyze various sensor and sensor network data collection of        various pre-identified individuals, and to select and implement        pre-identified actions based on those behaviors and/or        context-based triggers to maximize the potential of preventing a        violation or escalation of a violation.    -   Employ a risk-detection and action/activation system that        provides an “early-warning-system” for early detection of        adverse triggers such as “wrong or adverse” behavior, “wrong or        adverse” contexts, and/or “wrong or adverse behavior and        contexts”, and implementing a set of actions with appropriate        support resources to stop or “preempt” the parolee by pursuing        actions and/or activity that address the triggers (behavior,        context, or behavior/context pairings), before it reaches        violation status, or if violations have already occurred,        preempting any additional damage by the violator.    -   Use of big-data historical data collection and analytics        combined with various learning protocols, artificial        intelligence systems, neural learning networks, etc. to refine        each individual's trigger profile and associated sensors/sensor        settings, as well as continually improve actions and their        effectiveness in preempting unwanted behavior. Such        systems/methods can be used in designing and implementing more        nuanced or “tiered” reward/punishment systems (e.g., states        between being out on parole or back in prison, etc.), and/or in        gaining or losing points depending on the occurrence of        performance of pre-identified (good and bad) behavior and/or        activities.

There are many different embodiments of the present disclosure, but someof the more straightforward use an addiction-related crime, such as aDUI (Driving Under the Influence) arrest or a drug-trafficking charge.Such exemplary embodiments (generally, in increasing order of complexityand sophistication) may include:

-   -   1. A person on parole on DUI charges is prohibited from drinking        or driving under the influence. A profile is setup flagging        those activities, which in turn identifies key sensors to be        activated to collect data on Blood Alcohol Content (e.g.,        breathalyzers included in various devices including the        parolee's smartphone, required-wearing sensor bracelet, and in        the parolee's home (by the parolee's bed and bathroom sink),        etc.). All sensors are setup to collect any detection of alcohol        data and immediately report to a central monitoring center        (e.g., a rule violation is calculated locally), or the “raw”        data is sent to the center for processing and rule violation        checks. Any violation immediately results in a multi-media        message to a parole officer that includes the rule violation        information as well as supplemental information (e.g., where the        violation occurred, where the violator is at presently, and BAC,        etc.). Immediate steps are taken to “secure” the violator,        including disabling all of the violator's cars, notifying anyone        who has a restraining order against the violator, etc., as well        as activating sensor networks outside the violator's immediate        location to monitor the violator in case of attempted “escape”.        This includes GPS and/or various geofences (e.g., friends'        homes, etc.) to which the violator may attempt to escape on        foot.    -   2. In an exemplary embodiment, an Internet of Things (IoT)        network is established in the parolee's home, which besides        connecting to (all) the breathalyzers, also connects to sensors        on or near any alcohol (liquor cabinet, refrigerator, etc.). A        proximity rule is established prohibiting physical contact with        any alcohol-related container, such that not only liquor        cabinets/wine storage are activated but select refrigerator        sensors on all alcohol beverages (e.g., a bar code reader that        reads every bottle removed from the refrigerator, etc.), and        other bottles of alcohol-containing liquids (e.g., Listerine,        etc.). If the IoT/sensor network detects immediate proximity to        alcohol by the parolee or monitored person, or such products are        moving (e.g., via bar codes, RFID, or motion sensors, etc.), yet        there is no indication (yet) of an immediate violation (e.g.,        BAC content, alcohol-sensitive “smell” detectors, etc.), then a        message is sent out to the person's “support network” that        indicates that the person is (possibly) in imminent violation of        parole/release conditions. The underlying logic machine may then        analyze the location and other parameters of possible support        personnel (such a familiarity with the person's present        location) and directs them to immediately contact the person        and/or go to that person's location to prevent/preempt the        person from actually imbibing in alcohol and thus triggering a        violation. If, once the support person visits the monitored        person, the support person determines there was a potential        violation, but it was stopped in time, then appropriate negative        points/demerits are awarded to the monitored person. An        accumulated points level of X may result in increased visits        from a parole officer or some other punishment.    -   3. A parolee may be allowed outside the parolee's home for 2        hours from 2-4 p.m. but is not allowed within 100 meters of        certain places, such as schools or liquor stores. (Note: Current        GPS ankle bracelets often have variability in accuracy, up to        over 50 meters or more in certain circumstances). Thus,        “supplemental” readings are needed to collaborate a potential        violation. Also, in some situations a GPS ankle bracelet battery        life is a concern, thereby providing an incentive to conserve        battery life if possible. A “wearable” (e.g., bracelet, clothes,        hat, jewelry, etc.) with a specialized transmitter (that the        parolee or monitored person is required to have on his/her        person and active) transmits a signal at a certain frequency        (and with an identifier) when the parolee leaves home (e.g., it        is activated upon crossing a reverse geofence, along with the        GPS capability, etc.). The being at-home assurance is done by        the home's Internet of Things (IoT) sensor network that ensures        that the parolee is at home by using sensor/sensor networks,        such as motion sensors during daylight hours, and the use of        to-the-outside door sensors that activate/deactivate the GPS        bracelet when such an outdoor door is opened/closed.        -   Once outside, with the GPS activated and the proximity            transmitter transmitting, the parolee runs a variety of            errands. But if during the errands, the parolee or monitored            person comes close to a (e.g., forbidden, impermissible,            etc.) liquor store, a sensor at the store (which may be in a            mall or otherwise indoors along with other stores and thus            the GPS would not work well) detects/receives the signal,            and records time, place, and identifier, including whether            the parolee actually entered the store (e.g., via RFID or            other precise egress-detection means, etc.). Alternatively,            the bracelet or smartphone on/carried by the parolee detects            and records the presence of several Wi-Fi networks (e.g.,            from the various stores, etc.), along with signal strengths            and (if possible) directional information of the signals. A            logic program miming real-time or after-the-fact does a            real-time (local/edge) calculation or (possibly later) sends            the data to a central processing server, where a precise            location map and breadcrumb trail is developed and analyzed,            and determines whether a violation occurred, and if so            whether it was only a “technical” violation (e.g., the            parolee was within 50 meters, but did not enter the store,            etc.) or a “real” violation occurred (e.g., actually            entering the forbidden or impermissible store, etc.).            Because it is possible that the parolee had never entered            the mall before, the parolee may have not realized there was            a risk of the violation. Therefore, the system would perform            a historical analysis to identify any instances of            familiarity with the mall. If not, and a “technical”            violation, then the parolee may be let off with a warning or            some demerits. If a “real” violation did occur, the location            of the parolee is reported, and the parolee is taken into            custody and the body of location evidence used to prove            beyond a reasonable doubt that the violation indeed took            place.    -   4. An exemplary embodiment addresses GPS ankle-bracelet        tampering and related use cases. There have been numerous        incidents of bracelet tampering (which can be difficult to prove        that the tampering was deliberately done), or worse where the        bracelet was removed and the parolee went on a crime spree, and        where the parolee wasn't caught immediately because there was a        delay between the tampering and it being detected and acted upon        by the appropriate authorities. This exemplary embodiment may        thus address these scenarios as follows.        -   A detection of (possible) tampering can be difficult to            prove, as it historically relies on physical marks on the            bracelet as the key evidence. An exemplary embodiment            disclosed herein allows for increasing the body of evidence            to prove (or disprove) the tampering, premised on the fact            that tampering takes some time to successfully perform. In            this exemplary embodiment, the parolee bracelet has a            variety of additional sensors detect tampering (including            matching pre-identified sounds and vibrations with            measurements taken by the bracelet), as well as activating            other sensors outside the bracelet upon first (local)            detection of possible tampering. Other types of sensors            could anticipate potential tampering, such as elevated heart            rates or sweat levels.        -   The “matching” of sounds will result from internal bracelet            recordings of sounds and other sound/vibration-based            measurements taken when any sort of first indication of            tampering occurs, such as a sudden bump of the bracelet            (hitting it against something) or “clicking” (tampering with            a screwdriver). With that initial detection, internal            recordings of further sounds/vibrations are taken. In            addition, any surrounding networks (particularly parolee's            home network/IoT network) will be activated with a program            to activate/wake up or otherwise increase the frequency of            related readings (collectively “activating”) that could            prove (or disprove) the tampering. This includes activation            of cameras, audio and vibration sensors, and any other            indicators (such as sensors indicating prolonged stay in the            garage and/or tool usage).        -   In the case that the parolee doesn't care if the parolee is            detected, and cuts it off and takes off, another set of            activations occurs, with speed of the essence as well as            location precision (particularly for violent/sex offenders,            stalkers, or grudge-based or psychotic offenders). Once any            “full” tampering is conclusively (or with high degree of            certainty) detected (such as tampering warnings followed by            no motion by the bracelet yet other motion detected within            the home confinement area), a variety of actions would be            activated. The first set of activations would be focused on            “preemption”, e.g., preempting the possibility of escape as            well as harm to the public. This kind of preemption includes            disabling of any vehicles or automatic locking of doors            (e.g., actions that could be controlled locally and            activated real-time, etc.), to alerting of nearby (as            determined by other sensor networks) law enforcement or            support personnel (who while not having apprehension            authority, could help in finding and tracking the parolee            manually). Alerts would also go out to known places and            associates of the parolee, as well as Amber-Alert type            notifications (e.g., broadcast, etc.) to the parolee's past            victims and the general public, which could include photos            and videos and other descriptors.        -   In addition, communications with the cell phone carrier and            other communications providers of the parolee would be made,            and real-time “pinging” of the parolee's device(s) started,            as well as “activation” of any nearby (according to last            position) sensor networks that could potentially help detect            an on-the-run parolee. Real-time pinging allows use of            device GPS to provide real-time, precise locations, in            contrast with other methods that provide less accurate            cell-tower based location estimates. In the event the            parolee doesn't have such a device with the parolee, a            continually updating ring of sensor networks would assist in            obtaining at least a somewhat accurate location of the            parolee. Real-time pinging currently requires a court order.            Exemplary embodiments of the present disclosure envision a            streamlined process for obtaining such an order in near-real            time by packaging the evidence and presenting to a judge or            judge equivalent for sign-off, then activating a pre-setup            application interface to the carrier's systems to enable and            report real-time GPS pinging.        -   Any responses to the alerts would be automatically compiled            and analyzed, with a continually updated set of locations            provided to appropriate humans and other tracking            applications (particularly important if parolee is in a            vehicle despite the preempting efforts).        -   In any tampering scenario, it would be beneficial for all            concerned to preempt any tampering allegation, by            immediately providing feedback to the parolee that tampering            is being perceived, thus serving as a deterrent to further            attempts. This preempting could be provided via a variety of            scenarios, including alarms or vibrations from the bracelet,            automated messages via the parolee's smart phone, etc. The            interfaces could further include activating various            alarm-type mechanisms within the environment where the            parolee is at the time, such as the parolee's home (e.g.,            via an Internet of Things network, etc.), such as a locking            of doors or turning on of speakers or video to broadcast a            message, etc. A key part is to anticipate the appropriate            types of interface(s) to communicate with the parolee in            order to maximize its speed and effectiveness, such as            keeping “track” of what types of devices, mechanisms, and            interfaces are currently (or most recently used) by the            parolee, such as using the parolee's phone (immediately            sending text/MMS messages, instant messaging, or phone            calls; using the parolee's surfing the Internet (via desktop            computer) (immediately enabling a Skype/Facetime session);            watching TV (breaking in to the video feed with a            pre-broadcast warning message); sending messages (even            disorienting ones) to displays such as Google Glass;            activating pain/discomfort-based “messages” in the form of            shocks, intolerable noises, or even sedative drugs;            displaying pre-recorded (or real-time) holographic images by            doorways via projection mechanisms if the parolee is in the            process of leaving the house, etc. The key is to identify            and focus on the interfaces (and appropriate actions) that            will preempt/deter the potential violator from making things            any worse.        -   Once the parolee has been precisely located (either as a            result of the preemptive actions or “reactive” locating            efforts), the parolee may then be “followed” (e.g.,            continuing to report the location to law enforcement, etc.),            as well as continually calculating or anticipating possible            “future” locations (e.g., based on past behavior stored by,            obtained by, or accessed by this exemplary embodiment, etc.)            and activating appropriate preemptive inhibitors to escape,            such as if the parolee is approaching the home or residence            of the parolee ex-spouse and may have the ex-spouse's car            keys, in which the ex-spouse's car may then be disabled            according to exemplary embodiments disclosed herein.

There are numerous other exemplary embodiments associated with thispresent disclosure. Some of the more common violations that couldbenefit from such exemplary embodiments include possession, use, and/orintent to distribute (or arrange distribution of) controlled substances,theft, Driving under the Influence (DUI), aggravated assault (e.g.,assault related to original/prior convictions, particularlysex/child-related, other forms of assault, etc.), receiving stolengoods, etc.

Parolees and/or prisoners on some sort of controlled/temporary releaseare subject to significant rules that, if violated, will result inparole being revoked or modified, and/or privileges beingrevoked/modified. Such rules include but are not limited to curfewviolations, committing of (additional) misdemeanor/felonies, notreporting to a parole officer, crossing state lines or otherwise movingoutside prescribed boundaries, substance usage/abuse, associating withspecific person(s), violating court orders or other court mandatedboundaries, off-limits, or non-interaction, use of vehicles, etc. Manyof these rules/restrictions are location-oriented (and currentlydetectable), but behavioral violations are limited to rules that canonly be detected by specific, controlled tests (such as drug testing,etc.). Exemplary embodiments disclosed herein provide the ability todetect a much greater range and types of behaviors/contexts (triggers),allowing more flexibility and/or precision in rule settings by lawenforcement/judicial bodies, as well as the ability to monitor suchbehaviors to assist the parolee/prisoner in avoiding such behaviorsbefore they reach violation status. Such triggers that could potentiallybe monitored, measured, preempted, modified, and/or subject to violationinclude anger levels (e.g., relevant to general anger-based crimes,etc.), anxiety/pain-related issues (e.g., drug usage/theft, etc.),frustration-related specific issues (e.g., road range, etc.), excitementlevels (e.g., thrill seekers, etc.), hunger levels (e.g., theft, etc.),money issues (e.g., theft, drug distribution, etc.),associates/“friends” (e.g., peer pressure-related crimes, etc.),relative issues (e.g., various, particularly assault, etc.), sexualactivity (e.g., sexual crimes, etc.), etc. To the extent that aparolee/prisoner blamed a “certain thing” for causing the crime to becommitted, exemplary embodiments disclosed herein enable monitoring(e.g., by the court, etc.) of such a “certain thing” that historicallyhas not been otherwise detectable, measurable, or practical for sometechnological or other reason.

The collection and monitoring of such information, of course, has thepotential of being “big brother” on steroids, measuring and monitoringthe behavior of a person close to 24/7. To protect the rights andprivacy of those persons, it is critical to provide the appropriatelevel of security to such information. Unfortunately, today's securitymechanisms are vulnerable to hacking or other types of abuse. Thus,exemplary embodiments of the present disclosure may use a variety ofindividual and/or layers of security mechanisms and/or blockchaintechnology to record such transaction with the appropriate keys to“unlock” full data sets only to the appropriate persons and only forbehavior/location/context issues. Control of these keys/unlockingcapabilities will have many forms depending on the parolee, crime,triggers, and other factors, but in general multiple types of humans(and even machines) may be such data “stewards” such as law enforcement,judicial members, support members, medical personnel, etc. An exemplaryembodiment may include one or more systems and methods for providinglocation-based security and/or privacy for restricting user access asdisclosed above.

One exemplary embodiment may include monitoring for, preempting, and/ormitigating pre-identified behavior, particularly substance use asdisclosed herein. This exemplary embodiment may thus utilize methods andsystems disclosed above to preempt (to the degree possible) a relapse bythe parolee/prisoner, just as it would an addict, through variousmechanisms such as use of support personnel, safe spaces, andidentification and execution of actions as alternatives to drinking orusing drugs. A further distinction may be that in the event of a relapse(e.g., usage, etc.), various alarms may be triggered to appropriatelyinform law enforcement personnel, but ideally before this would happenutilizing the preemptive measures and support resources of the presentdisclosure to prevent the relapse from occurring or worsening.

An exemplary embodiment includes the detection and preemption ofactivities associated with associating with disreputable persons,potentially including an increased risk of performing a violation withthose persons such as distributing drugs. This exemplary embodimentdraws not only on the location of the parolee/prisoner but extends thatknowledge to anticipating the context (what they are doing, how they aredoing it) in addition to the location (where, and when), to understandthe why. This is supplemented with external data sources, such as socialmedia postings and purchases, and (if available) similar informationfrom those “associates” to compare the data sets to detect anypotentially rule-breaking associations.

An exemplary embodiment includes the detection and preemption of drivingunder the influence (of drugs or alcohol), a major cause of paroleviolations. The sensors in, on, and/or associated with the person willenable detection of an “in progress” violation, not only by“traditional” methods (e.g., blood alcohol content or similar methods,etc.), as well as detection of location, context, and/or behavior that“anticipates” such behavior, such as driving near/stopping by a liquorstore or by known areas of drug dealing, as well as combining suchinformation with locations of other persons known to be associated withthe person's prior violations. The location of such 3^(rd) persons couldbe determined by similar or other tracking mechanisms, as well asdetection of such 3rd person's proximity “signatures” in the form oftheir device transmissions, attempts at peer-to-peer communications,monitoring of social media location postings, etc. Such “mashups” enableearly detection of high-risk situations that could avoid an actualviolation (and associated potential damage) Support can take many forms,such as automatic facial recognition.

One form of unique identification is facial recognition. An exemplaryembodiment may include the use of crowdsourcing image/videorecognition/profile scanning to extend the reach of a victim's network.For example, the victim's device could connect to any devices withinrange of the victim's device and use their image/video/profile scanapplications (obtaining distinct, even unique elements in a person'sfacial structure) to “scan” the environment around those devices. Inturn, the images/videos/profile scans would be compared to data for theperpetrator to identify a match or probability of a match, and reportthis back to the victim. This concept can be extended to where thevictim is going in the future, in order to obtain an “all clear” status.Such information (e.g., perpetrator in the vicinity of the victimdespite the court order, etc.) is anticipated to be used to proveviolations in court.

An exemplary embodiment includes the prevention/preemption of theparolee/prisoner from coming in contact with banned persons (e.g., courtorder, restraining order, etc.) or types/classes of people (e.g., minorchildren, etc.). This exemplary embodiment may enable more preciselocation determination via non-GPS technologies, allowing morespecificity in court orders as well as evidence in violations. It alsoallows variations in behavior in court orders in addition to or insteadof location, such as proscribing the non-allowance of Anger (withassociated readings such as blood pressure and skin temperature levelscombined with proximity to the spouse, for example), as well as earlydetection of “in progress” violations such as rising Anger that wouldtrigger the intervention of support personnel to prevent Anger frompassing certain thresholds and thus trigger a violation. Sexual paroleesor those convicted but have served their time, but still have toregister as a sex offender, could be monitored as part of their release.Sensors in this exemplary embodiment may be used to detect risingtestosterone levels, or interfaces with mobile and/or fixed devicescould monitor web activity to detect any porn or sexual content relatedto the original offense(s). Such sensor readings/activity could triggersupport response to intervene before levels reach a point where theperson reaches violation status (such as being tempted to get inproximity to a school). Alternatively, such “pre-crime”readings/activity could initiate a medical response such as automaticadministering of testosterone-reducing drug, for example, or othermedicine designed to reduce sexual desire. If such support/medicalresponse(s) in response to a rising risk of “relapse” are notsuccessful, then an alarm could be sent to the appropriate authority toapprehend the person for temporary “safe-keeping” until the risk haspassed. The information sent would include the location of the personand the nearest “safekeeping” facility, which may or may not be a formalincarceration facility.

While the use of bracelets and other “wearable” technologies for thetracking of individual persons with specific location-related crimeand/or parole-oriented limitations is known, there are many situationswhere the person involved has not actually been charged with a crimeand/or the accused is not legally enforceable to include such an objecton or near their person. The use of Restraining Orders to preventstalking and other personal-interaction crimes and issues is an example.While victims of such personal violations can obtain such orders,generally the evidence of an actual crime is not egregious enough (ornot criminal in nature) to warrant a judge ordering the stalker to wearsuch an object. This exemplary embodiment may enable the ability totrack the “criminal” in a way that does not require such a wearableobject. There are ways to do this via exemplary embodiments disclosedherein. A first way is for a court or other entity to order that anapplication be activated (associated with this disclosure), and to beput on the perpetuator's phone and other devices. The above instance mayhave a couple limitations, such as requiring access to (and permissionof) the perpetuator's devices for such applications to be installed. Thefunctionality of this part of the capabilities on the perpetuator wouldinclude monitoring the location, context, and/or behavior of theperpetuator as it relates specifically to the victim.

For example, in the case of ex-spouses/partners etc. it may be that thevictim is most at risk in cases where the perpetuator becomes in anangry state, and/or when drinking Other aspects described herein wouldseek to monitor/detect. While a court can do this, there are ways aroundit, notably obtaining other devices (e.g., replacement phones, etc.), orto just not use or turn off devices with the application on it. While itis possible to detect such “active non-usage”, it would be cumbersome tomonitor and report said non-usage and to seek remedies. A better, ifpotentially less precise way of generating a “perpetuator alarm” is touse the sensors and other detection capabilities around the victim inorder to detect the presence of the perpetuator. Specifically, thevictim will “calibrate” the sensors and other controllable technologiesto look for a “signature” of the perpetrator and use that to warnhim/her of a rising risk situation. Such a signature could come in manyforms. One form may include an identifier id or node address thatuniquely identifies the perpetuator, either “truly unique” (such as aphone SSID or unique bracelet/wearable Identification Number, etc.), ora “relative” unique address that enables clear identification within thecontext of the victim, such as the victim's network, peer-to-peercommunications with the victim's device, a “victim network”, a“perpetuator network”, a “crime” network (e.g., theft inMissouri/Illinois, etc.) or some other network that in combination witha device identifier is able to uniquely identify the perpetuator.Various other limitations could be included such as time-basedparameters that would both help to uniquely identify the perpetrator aswell as provide a degree of privacy (e.g., the perpetuator would only beidentifiable while the court order was in force, etc.). Because suchunique IDs are either not broadcast or otherwise communicated outside acellular network (such as a SSID), such a unique ID could be obtained invarious other ways.

An exemplary embodiment may include the use of beacons. Historically,beacons have been used as one-way transmitters, typically attached to awall in a room or other defined space and used to trigger sensors in aphone or badge. Because beacons generally have limited range, a phone ordevice receiving a beacon transmission is by definition in the vicinityof the beacon, such as in a particular room, etc. The phone/device canthen by itself or with other location determination systemsinfrastructure report the location to the infrastructure or other typesof location services.

Exemplary embodiments may use the above type of real-time locationservice, but it is generally limited to a defined space, such as ahospital or campus, etc. As such, it has limited utility in terms ofwarning a victim if a perpetrator is anywhere else but that campus,hospital, or other defined space. Accordingly, exemplary embodimentsdisclosed herein may include a beacon on, in, or around theperpetuator's device, and will use that infrastructure to broadcast asignal uniquely identifying the perpetuators device. Once properlyequipped, the perpetuator will be unable to silence the beacon(potentially even while off) and will likely have longer range thantoday's beacons. In fact, the beacon range could be set to apredetermined distance consistent with a court ruling, e.g., not within1000 feet of the victim, etc. In turn, the victim's device(s) will beset to detect such beacons, as well as the individual perpetrator's' ID,which if “heard” by the victim's device means that the court order wasviolated. And, in turn, the victim's device can automatically inform lawenforcement, which can then use its own methods for precisely locatingthe perpetrator. A refinement could be a 2-way beacon in theperpetrator's device, which could inform perpetrator that theperpetrator is violating the order (presumably it being doneinnocently/inadvertently), so that the perpetrator can quickly move awayfrom the victim. For example, the perpetrator could perhaps be given a2-minute lead time before the perpetrator is considered to be inviolation.

An exemplary embodiment may utilize the “natural” tracking capabilitiesof wireless carriers. As part of their normal function, carriers need tokeep constant track of where every subscriber is (that has theirdevice(s) powered on), to be ready to connect at a call, text, or datasession at any time, to be able to hand off to the nearest availabletower when the subscriber is moving, and for billing purposes. The partof the process that tracks the subscriber (specifically, tracks the celltower that the subscriber is connected to at any one time) is throughthe use of a Home Location Register (HLR) and a Visitor LocationRegister (VLR). As part of the call setup process, the HLR and VLR arechecked and updates to reflect the device's location. An exemplaryembodiment may modify this process in at least one of several ways. Thefirst is once the carrier is informed that a subscriber has a courtrestraining order against the subscriber that has a location limit(s) toone or more location and/or other devices/subscribers, an indicator ofsuch an order may be provided, in the form of a flag or other databasetable element, to indicate there is such an order. Additionalinformation could also be included in the HLR/VLR, such as duration ofthe order, the locations/devices/subscribers they involve, and therespective location ranges. Every time a carrier “sees” a request tosetup a call or terminate a call, if such a flag is set, the carrierwill output the device's location to an external software interface,(possibly) along with the additional information such asdevices/numbers/IMEI of persons who may need to be notified. A locationlookup of the victim(s) phone current location may then be initiated, aswell as other potentially applicable locations (e.g., victim's home orwork, school, etc.), and compare the locations and determine if theterms of the order are being violated, such as being within 1 mile ofthe victim or the victims/home or work. If so, appropriate action couldbe taken by the applicable law enforcement, but on an immediate basisthe victim will know to move their own location immediately to stay outof harm's way.

One issue with this approach is while the victim's location can beprecisely determined by the GPS in their device, the perpetuator's phoneis being located by cell tower. There are several potential solutions toenhance the perpetuator's accuracy. The first is for the victim softwareto execute a “network initiated” request to the carrier for a moreprecise location. This could be done through location pingingcapabilities already done for law enforcement purposes, or,alternatively, using an application similar to those used by familytracking applications. Another is utilizing the software and/or methodsdescribed earlier that resides on the perpetrator's phone. In thisinstance, the idea is that the HLR/VLR serves as an “early warningindicator” for the victim, and also as a legal initiating to a processto obtain a more precise location, through various means, e.g., Wi-Fi,RFID, beacon, RTLS, etc.

An exemplary embodiment may be configured to be involved with stalkingor boundary sentences such as not leaving the state. The detection ofabsconding or otherwise leaving an area that a court has required aperson to stay within is generally a straightforward process and systemin the form of a geofence and a reporting of a violation of thatgeofence to the appropriate authorities. But preempting such a violationor anticipating it in the act (before crossing the geofence boundary) isanother matter, one addressed by exemplary embodiments disclosed herein.Once a parolee/prisoner is anticipated to be at or above some level ofabsconding risk (generally at time of parole/release), tracking ofmovements and behavior begins using applications on the person's phoneand potentially on wearables (e.g., bracelet, etc.) mandated to be worn.Such devices/wearables can generate a significant amount of data aboutmovements/activities of the person, and over time can begin todetect/develop patterns as to how the person is living their lives. Amaterial change in this activity can be an indicator of uncertainty orconfusion on part of the person—a mindset that often precedes serious,non-common-sensical actions like fleeing the area (absconders are nearlyalways hit with a much worse penalty than they would have received ifthey had stayed for the original offense sentencing).

But “forcing” a person who has little desire to wear something or allowoperation on their phone is a real problem, particularly if remediesrequire the victim constantly going back to court to enforce the termsof the restraining order. If the perpetuator can be monitored withoutactive involvement on their part, this would be ideal. Exemplaryembodiments disclose such capability, e.g., requiring broadcasting of aspecific signature/signal, or requiring the cooperation of the wirelesscarriers involved.

An exemplary embodiment involves violations/crimes associated with thepossession of a gun despite a court order. Detection of such a gun canbe done by an exemplary embodiment directly or indirectly. Directly hasto do with special locks/sensors attached to gun cases of guns in thepossession of the person. An exemplary embodiment enables “tripping” ofsuch sensors to send alarms to the appropriate support personnel (ifsuch intermediate interventions are allowed in such cases) so that thesupport personnel can follow-up and see if there is a legitimate reasonfor the case to be opened/tampered with.

An indirect method includes detection of locations, sounds, orassociated “damage” associated with guns and gun shots. Detecting theperson's presence at a gun range is one such potential indicator as arenoise detectors that register a gunshot sound (in terms of loudness ortype of noise). Sensors tuned to hand residue could detect gunpowder.Any one or more or all of these could be utilized in detecting an “Inprogress” possible violation that can then utilize the support networkto intervene and failing that notifying the appropriate authorities orsupport personnel.

An exemplary embodiment involves bail jumping/bond violation andincludes detection of and tracking down of bail jumpers (persons on bailthat do not meet their court appearance). Such people generally aredivided into 2 classes: 1) Accidental, or 2) Deliberate. Accidental arejust that—they forgot about their court date. In these cases, the bountyhunter has to find the person and bring the person into court to getrebonded and rescheduled. An exemplary embodiment disclosed herein maybe tuned to automatically detect such accidental violations andimmediately inform bondsman (and hunters) of such violations, as well asthe current location/context/behavior and likelylocations/contexts/behavior during the next 24-48 hours, based on theperson's history since the original bonding, which is tracked via an appon the bondee's phone as a condition of getting the bail. Thus, if thefailed bondee usually is bowling Wednesday night, that is likely wherethe bondee will be the Wednesday after the missed hearing, and thebounty hunter can make appropriate arrangements based on that bowlingcontext. For example, the bounty hunter may wait until after the bondeeis finished bowling to avoid embarrassment in front of the bondee'sbowling buddies, but before the bondee gets home and the associatedembarrassment of arrest in front of the bondee's children.

Exemplary embodiments disclosed above are examples of previousviolations/conviction-based location, contest and behavior monitoring,tracking, preempting/prevention, and relapse reporting. But there couldalso be numerous variations depending on a variety of factors includingage (e.g., juvenile, senior, etc.), type of conviction (e.g., felony vsmisdemeanor, etc.), level of crime, number of convictions, etc. History(of drug abuse, alcohol abuse, etc.), sexual history/background, whetherthey are currently employed, degree of financial stability, how longsince they last contacted high risk associates, whether they are incounseling and how long, have their own residence and how long (and withwhom and associated stability of such persons, etc.), have their owntransportation, are in a stable relationship and how long, how longthey've been paroled/under supervision, number of convictions, totalyears served, number of uneventful previous parole violations andproblem-free parole years, number/types of previous parole violations,etc. Family and friends support structure and/or the type and degree ofcounseling/treatment are very important risk elements. For example, aDUI offender currently in closely (separately) monitoredresidential/inpatient rehab could be consider low risk, low frequencymonitoring up to the time until leaving the treatment facility. At thatpoint, their housing and family/friend support structure andemployment/training prospects could keep the risk low (if those elementsare all favorable) but shoot to high if the home life they are going(back) to is very unpleasant. In which case, it would immediately raiseto high risk/high monitoring levels. Other treatment types such asOutpatient, Halfway Houses, and Day Reporting could also have associatedrisk elements.

Some or all of the above could be computed into a Risk Profile and/or ascoring system that could “calibrate” the sensitivity ofmonitoring/tracking/analysis algorithms and algorithms/methods forinitiating support of the preemptive actions of possible high-riskviolations. Behavior monitoring could even reach areas such as dietmonitoring, in case for example low/high blood sugar has beendemonstrated to be a high-risk factor of erratic behavior on part of theparolee/prisoner.

In exemplary embodiments, all of the above may be incorporated intodifferent intensities of monitoring. A simple low/med/high intensityschema could serve as a non-limiting tiering.

Exemplary embodiments of the present disclosure can be implemented innumerous ways, including (without limitation) as method(s)/process(es),apparatus(es), system(s), composition(s) of matter, computer readablemedia, such as non-transitory computer readable storage media, and/orcomputer network(s) wherein program instructions may be sent, e.g., overoptical, electronic, wireline, cloud-based, drone-based, Internet,wireless, peer-to-peer, machine-to-machine, and/or other communicationslink(s) and combination(s). At least some such implementations may bereferred to, e.g., as techniques and/or mechanisms. In general, theorder of the steps of disclosed processes may be altered within thescope of the present disclosure.

Exemplary embodiments are disclosed of systems and methods formonitoring for and preempting pre-identified restrictionviolation-related behavior(s) of persons under restriction, such as aparolee, a prisoner, a person on probation, a person under house arrest,a person under a restraining order, a person under supervision and/orrestriction(s) ordered by one or more of a criminal court, a civilcourt, a family court, and/or another justice entity, and/or a personunder supervision and/or restriction(s) imposed by an association, anentity, and/or an organization, etc. In an exemplary embodiment, asystem generally includes a plurality of different devices, sensors,sensor arrays, and/or communications networks. The system is configuredto: determine, through a plurality of measurements/readings taken by theplurality of different devices, sensors, sensor arrays, and/orcommunications networks, behavior(s) of a person under restriction andcontext(s) associated with the behavior(s) of the person underrestriction; assess, evaluate, and predict a risk of a futureoccurrence(s) of a pre-identified restriction violation-relatedbehavior(s) and associated context(s) by the person; and facilitate oneor more pre-identified actions and/or activities to preempt and/or lowerthe risk of a future occurrence(s) of the pre-identified restrictionviolation-related behavior(s) by the person under restriction before aviolation occurs.

The person under restriction may be one or more of a parolee, aprisoner, a person on probation, a person under house arrest, a personunder a restraining order, a person under supervision and/orrestriction(s) ordered by one or more of a criminal court, a civilcourt, a family court, and/or another justice entity, and/or a personunder supervision and/or restriction(s) imposed by an association, anentity, and/or an organization By way of example, the person may beunder supervision and/or restriction(s) imposed by Alcoholics Anonymous(AA), another organization disclosed herein, other non-govermentalorganization that is not associated with the government or justicesystem, etc.

When the person under restriction is a parolee, the system may beconfigured to: determine, through a plurality of measurements/readingstaken by the plurality of different devices, sensors, sensor arrays,and/or communications networks, behavior(s) of the parolee andcontext(s) associated with the behavior(s) of the parolee; assess,evaluate, and predict a risk of a future occurrence(s) of apre-identified parole violation-related behavior(s) and associatedcontext(s) by the parolee; and facilitate one or more pre-identifiedactions and/or activities to preempt and/or lower the risk of a futureoccurrence(s) of the pre-identified parole violation-related behavior(s)by the parolee before a parole violation occurs.

The system may be configured to determine whether there is a variationfrom a defined set of conditions and/or allowed activities for theperson under restriction based on the behavior(s) and associatedcontext(s) of the person under restriction, whereby the variationindicates a risk of a future violation by the person under restriction.The defined set of conditions and/or allowed activities may comprise oneor more of permitted travel, permitted location(s), permitted locationdwell time, permitted travel path(s), proximity to a prohibitedperson(s), and/or proximity to a prohibited location(s).

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may be configured to collect and/or reportdifferent types of data for the person under restriction. The system maybe configured to compare the data collected and/or reported by theplurality of different devices, sensors, sensor arrays, and/orcommunications networks with reference behavior data predefined for theperson under restriction. The reference behavior data predefined for theperson under restriction may comprise one or more of: criminal behaviordata for the person under restriction; criminal history and criminalrecord data for the person under restriction; data relating to a numberof different types of crimes; probability data that compares variouscrime types with various location types wherein a crime probability foreach of the various crime types is determined and assigned for each ofthe various location types; and/or restraining order history andrestraining order record data for the person under restriction.

The system may be configured to determine, project, or predict a currentor future context of the person under restriction at a location byanalyzing and linking real-time data and historical data for the personunder restriction, the real-time and historical data including thelocation of the person under restriction, historical context of theperson under restriction at the location, behavior patterns, travelpatterns, health data, and risk calculations.

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may allow the system to monitor a location ofthe person under restriction both indoors and outdoors.

When the person under restriction is a parolee, the system may beconfigured to: determine, through the plurality of different devices,sensors, sensor arrays, and/or communications networks, a location ofthe parolee and the context of the parolee at the location; assess,evaluate, and predict a risk of a parole violation by the parolee inrelation to the location and the context; and facilitate one or morepre-identified actions and/or activities to preempt and/or lower therisk of the future parole violation.

The system may be configured to determine whether one or more triggersindicative of a risk of a future violation by the person underrestriction and predetermined in the system are active or present basedon the behavior(s) of the person under restriction and the context(s)associated with the behavior(s) of the person under restriction, asdetermined through the plurality of different devices, sensors, sensorarrays, and/or communications networks. The system may be configured todetermine whether any of the one or more triggers predetermined in thesystem are active or present by comparing data from one or more of theplurality of different devices, sensors, sensor arrays, and/orcommunications networks with one or more settings for the person underrestriction. The one or more settings for the person under restrictionmay include one or more of blood pressure, heart rate, skin temperature,body temperature, respiratory rate, perspiration, weight, exerciseschedule, external temperature, noise levels/loudness, and/or noisetypes/frequency(ies). The plurality of different devices, sensors,sensor arrays, and/or communications networks may comprise one or morebiometric, environmental, and/or behavioral sensors that provide thebiometric, environmental, and/or behavioral data for the person underrestriction usable by the system in determining whether any of the oneor more triggers predetermined in the system are active or present. Thesystem may be configured to receive and process feedback and to adjustthe plurality of different devices, sensors, sensor arrays, and/orcommunications networks including increasing, decreasing, and/orotherwise modifying one or more of the settings and/or a frequency ofdata collection in response to the feedback including actions, contexts,and behaviors of the person under restriction associated with the data.

The system may be configured to: determine, through the plurality ofdifferent devices, sensors, sensor arrays, and/or communicationsnetworks, a location of the person under restriction and the context ofthe person under restriction at the location; and determine whether oneor more triggers indicative of a risk of a future violation by theperson under restriction and predetermined in the system are active orpresent based on the location, the context, and one or more ofbiometric, environmental, activity, and/or behavioral data for theperson under restriction.

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may include one or more of a local network, apublic network, a private network, the internet, the Internet of Things,a wireless network, a terrestrial network, a cloud network, a Bluetoothnetwork, a beacon network, a cloud network, a peer-to-peer network, adrone network, a Zigbee network, a satellite network, and/or wirelinenetwork. The context(s) include a situation, an environment, and/or astate of mind of the person under restriction based on one or more ofbiometric, environmental, activity, and/or behavioral data of the personunder restriction.

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may include: a plurality of sensors configuredto monitor a location and/or the context(s) of the person underrestriction at the location, one or more of the plurality of sensorsbeing located in, on, and/or near the person under restriction; and aplurality of interface devices configured to engage in interaction withthe person under restriction, with one or more support persons for theperson under restriction, and/or with one or more third parties in theevent the system determines a relationship between the location and/orthe context(s) and one or more triggers predetermined in the system thatindicates a risk of a future occurrence(s) of the pre-identifiedrestriction violation-related behavior(s) by the person underrestriction. The system may be configured to select the interactionbased on the one or more triggers and the location and/or the context(s)of the person under restriction at the location.

The system may be configured to develop and/or update a profile of theperson under restriction including one or more predetermined actions toimplement for the person under restriction depending on the predictionand evaluation of the risk of an occurrence(s) of the pre-identifiedrestriction violation-related behavior(s) by the person underrestriction. The system may be configured to be usable by another one ormore persons to voluntarily and/or involuntarily monitor a location ofthe person under restriction, the behavior(s) of the person underrestriction, and the context(s) of the person under restriction at thelocation. The one or more pre-identified actions and/or activitiesfacilitated by the system may include one or more of disabling a vehicleof the person under restriction and/or providing an alert to one or moreof law enforcement, a support person, or other person.

The system may be configured to assess, evaluate, and predict, a risk ofa future occurrence(s) of a pre-identified restriction violation-relatedbehavior(s) and associated context(s) by the person under restrictionbased on historical location-based data for the person underrestriction.

The pre-identified restriction violation-related behavior(s) may includeone or more pre-identified behavior patterns, one or more pre-identifiedbehavior trends, one or more pre-identified contextual patterns, one ormore pre-identified contextual trends, and/or one or more pre-identifiedtriggers. The system may be configured for monitoring for and preemptingthe one or more pre-identified behavior patterns, the one or morepre-identified behavior trends, the one or more pre-identifiedcontextual patterns, the one or more pre-identified contextual trends,and/or the one or more pre-identified triggers of the person underrestriction.

The context(s) associated with the behavior(s) of the person underrestriction may comprise at least one or more interrelated conditionsincluding situations, circumstances, events, environment, activities,and/or actions being done by, associated with, and/or around the personunder restriction, time, and/or location(s) of the person underrestriction as determined through the plurality of different devices,sensors, sensor arrays, and/or communications networks.

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may be configured to collect and/or reportdifferent types of data for the person under restriction includingbiometric, environmental, activity, and/or behavioral data. The systemmay be configured to use the data collected and/or reported by theplurality of different devices, sensors, sensor arrays, and/orcommunications networks that meets one or more pre-identified criteria.

The system may be configured to determine behavior(s) of the personunder restriction and context(s) associated with the behavior(s) of theperson under restriction by a comparison of data from the plurality ofdifferent devices, sensors, sensor arrays, and/or communicationsnetworks with one or more settings for the person under restriction.

The system may be configured to allow a user to modify an identity ofthe person under restriction, the pre-identified restrictionviolation-related behavior(s) and the associated context(s), thepre-identified actions and/or activities, and/or the plurality ofdifferent devices, sensors, sensor arrays, and/or communicationsnetworks.

The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may comprise at least one sensor array includinga plurality of different types of sensors operable for taking aplurality of different types of measurements/readings usable by thesystem to determine the behavior(s) of the person under restriction andthe context(s) associated with the behavior(s) of the person underrestriction. The plurality of different devices, sensors, sensor arrays,and/or communications networks may include one or more social networks.The plurality of different devices, sensors, sensor arrays, and/orcommunications networks may include one or more devices, sensors, and/orsensor arrays remote from the person under restriction.

The system may be configured to use, in combination, the plurality ofmeasurements/readings taken by the plurality of different devices,sensors, sensor arrays, and/or communications networks to determine whena violation trigger is being activated and/or when a potential violationis in progress through a pre-identification process and an iterativemachine learning/artificial intelligence process incorporating humaninput of high risk chance of violation situations, to thereby enable thesystem to proactively and preemptively detect high-risk violationsituations for the person under restriction.

The system may be configured to: determine whether a location and thecontext(s) correspond to a high-risk location and context(s) and/orcorrespond to a trending risk of the pre-identified restrictionviolation-related behavior(s); identify one or more potential actionsand/or available support resources to preempt and/or lower the risk of afuture occurrence(s) of the pre-identified restriction violation-relatedbehavior(s); select one or more actions and one or more interfaces forthe person under restriction; and implement the selected action(s) andinterface(s) for the person under restriction.

The system may be configured to restrict and condition access to datafor the person under restriction collected by the plurality of differentdevices, sensors, sensor arrays, and/or communications networks based ona user's selection of location-based data for the person underrestriction from a plurality of options presented by the system forselection. The plurality of options may include the location-based dataand one or more other options that are selectable by the user.

The system may be configured to determine at least one historicallocation of the user and at least one historical context of the user atthe at least one determined historical location. The system may beconfigured to, in connection with the determining the at least onehistorical location and the at least one historical context: determinethe at least one historical location based on data obtained through atleast one location sensor; and determine the at least one historicalcontext based on data obtained through the at least one location sensorand at least one different sensor. The system may be further configuredto: receive a request for access by the user to data for the personunder restriction obtained through the plurality of different devices,sensors, sensor arrays, and/or communications network; present one ormore queries or qualifiers to prompt the user to select at least one ofthe plurality of options in response to the one or more queries orqualifiers; and present the plurality of options for selection by theuser. The plurality of options may include location-based data for theuser and the one or more other options. The location-based data for theuser may include data based on the determined at least one historicallocation of the user and the determined at least one historical contextof the user. The system may be configured to include the location-baseddata for the user in the at least one of the plurality of options basedon a consistency between (a) the one or more queries or qualifiers and(b) the determined at least one historical location for the user and thedetermined at least one historical context for the user. The system maybe configured to allow the requested access by the user to the data forthe person under restriction based on the user's selection of at leastone of the plurality of options including the location-based data forthe user, whereby the user is the person under restriction, anotherperson, and/or an accessor.

The system may be configured to determine the at least one historicalcontext of the user at the determined at least one historical locationbased, at least in part, on data obtained through at least one biometricsensor. The system may be configured to present the one or more queriesor qualifiers to prompt the user to select at least one of a pluralityof options in response to the one or more queries or qualifiers, basedon a memory profile for the user. The memory profile may include one ormore user-specific preferences for a query or qualifier.

The system may be configured to select one or more sensors through whichto obtain the data to determine the at least one historical location ofthe user and the at least one historical context of the user, based on amemory profile for the user. The memory profile may include one or moreuser specific preferences defining one or more types of sensors throughwhich the data used for determining the at least one historical locationand the at least one historical context of the user at the at least onedetermined historical location is obtained.

The system may be configured to: predict, based on the determined atleast one historical location of the user and the determined at leastone historical context of the user at the determined at least onehistorical location, at least one future location of the user and atleast one future context of the user at the predicted at least onefuture location; and present the plurality of options for selection bythe user. The plurality of options may include the location-based datafor the user, the location-based data for the user based on thepredicted at least one future location of the user and the predicted atleast one future context of the user at the predicted at least onefuture location.

The person under restriction may comprise a group of at least twopersons under restriction. The system may be configured for monitoringfor and preempting pre-identified restriction violation-relatedbehavior(s) of the group of at least two persons under restriction. Inthis example, a violation by any one person of the group may be deemedto be a violation for the entire group.

The group of at least persons under restriction may comprise a group ofat least two children under restriction. In this example, the childrenin the group may be related (e.g., siblings, cousins, etc.) or notrelated. And, the system may be configured for monitoring for andpreempting pre-identified restriction violation-related behavior(s) ofthe entire group of children instead of a single child.

The group of persons under restriction may comprise a group of at leasttwo persons living together (e.g., a married couple, unmarried personscohabitating, friends living together, etc.) who are under restriction(e.g., on parole for the crime, under the same restrictions, etc.). Thesystem may be configured for monitoring for and preemptingpre-identified restriction violation-related behavior(s) of the group ofat least two persons living together.

The group of at least two persons under restriction may comprise a cellblock of prisoners outside on work detail. In this example, a violationby a single prisoner of the cell block of prisoners may be treated as ordeemed to be a violation for the entire cell block of prisoners, which,in turn, may then have repercussions for the entire cell block ofprisoners. For example, a violation by a single prisoner may result inthe revocation or loss of outside work detail for the entire cell blockof prisoners. An exemplary embodiment may include a cell-block-onlynetwork and sensor array configured to monitor for pre-identifiedrestriction violation-related behavior(s) of the cell block ofprisoners, e.g., monitor for predetermined action(s) targeted at thecell block of prisoners, etc.

Also disclosed are exemplary methods for monitoring for and preemptingpre-identified restriction violation-related behavior(s) of personsunder restriction. The method generally includes determining, through aplurality of measurements/readings taken by a plurality of differentdevices, sensors, sensor arrays, and/or communications networks,behavior(s) of the person under restriction and context(s) associatedwith the behavior(s) of the person under restriction; assessing,evaluating, and predicting a risk of a future occurrence(s) of apre-identified restriction violation-related behavior(s) and associatedcontext(s) by the person; and facilitating one or more pre-identifiedactions and/or activities to preempt and/or lower the risk of a futureoccurrence(s) of the pre-identified restriction violation-relatedbehavior(s) by the person under restriction before a violation occurs.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprises computer-executable instructions for monitoring for andpreempting pre-identified restriction violation-related behavior(s) of aperson under restriction, which when executed by at least one processor,cause the at least one processor to: determine, through a plurality ofmeasurements/readings taken by a plurality of different devices,sensors, sensor arrays, and/or communications networks, behavior(s) ofthe person under restriction and context(s) associated with thebehavior(s) of the person under restriction; assess, evaluate, andpredict a risk of a future occurrence(s) of a pre-identified restrictionviolation-related behavior(s) and associated context(s) by the person;and facilitate one or more pre-identified actions and/or activities topreempt and/or lower the risk of a future occurrence(s) of thepre-identified restriction violation-related behavior(s) by the personunder restriction before a violation occurs.

There has been a recent explosion in awareness regarding historical andcurrent sexual harassment as evident by the ongoing falls from power ortravails of prominent celebrities, politicians, government officials,businessmen, etc. While the widespread movement and the disgrace ofvarious individuals may be justified, the broader ramifications, sideeffects, and “unintended consequences” on society and interpersonalbehaviors and dynamics have also become apparent. One of these sideeffects is in “dating”—the seeking of and “executing” of potentialromantic and/or sexual relationships. There have been numerous newsstories where a person apparently said “Yes” to an invitation to asexual encounter at some point (often early) in a relationship—at leasta “Yes” as interpreted by the other person—that once concluded(attempted or completed), the person has changed their view on whathappened, why, or both with assertions, e.g., “took advantage of me whenI was drunk”, etc.

Previously, such a “defense” may have been waved off on the allegedgrounds that “the person knew what they were doing” or “as long as theperson was conscious then what is the problem?”. More recently, theslightest hint that one person took advantage of another person in someway during a sexual (or even non-sexual dating) encounter may be treatedharshly by society and increasingly so by law enforcement. In suchsituations, the accused party has heretofore had very little defensebeyond “he said, she said” and with the “accuser said” typicallyreceiving more weight than before. Even in situations where the accusedhas been cleared (e.g., college campus tribunals where the weight ofevidence is less than criminal court, etc.), the accusation—often fueledby social media—may never really go away and may follow the clearedaccused person like a tattooed Scarlet Letter.

Exemplary embodiments include systems and methods for developing,monitoring, and enforcing legal, technical, and social agreements,understandings, and/or contracts (e.g., legal, common law, or“handshake-like” agreements, etc.), such as by using behaviors and/oractions (e.g., pre-identified behaviors, preemptive actions, etc.)determined via one or more different devices, sensors, sensor arrays,and/or communications networks (e.g., the Internet of Things (IOT),social networks, etc.). As disclosed herein, behaviors and contexts maybe monitored by sensors, sensor arrays, devices, and/or communicationsnetworks including wireless, wireline, social networks, and/or TheInternet of Things (IoT) and can include physical conditions,activities, and mental thought processes of pre-identified individualsin a variety of situations or circumstances (“contexts”). Currentbehaviors and, as applicable, contexts of pre-identified individuals arecompared to pre-identified behaviors and, as applicable, contexts andassociated acceptable variances to determine whether or not currentbehaviors/contexts as well as behavior/context trends conform withinaccepted boundaries of the behaviors/contexts. Based on if/degree ofconformity, inclusiveness, fit, or other determinations, actions areinitiated to either (1) “approve” the behavior in the form of some sortof contract, agreement, or understanding along with associatedfollow-up, measurement, and/or monitoring to ensure compliance/adherenceto the agreement(s), or (2) alternatively, initiate pre-identifiedactions or artificial intelligence (AI) developed or othermachine-determined actions to deter, stop, improve, or mitigate thebehavior/contexts from continuing in their present (“unapproved”) form.

In exemplary embodiments disclosed herein, a trigger may be generallydefined as or include behavior and (optionally) context. Behavior may begenerally defined as or include the way or manner that someone conductsthemselves or behaves (whether in public or private). Context may begenerally defined as or include the interrelated conditions in whichbehavior occurs, exists, or takes place, such as situations,circumstances, events, activities, actions, location, and/or time(year/month/day and hour/minute).

As recognized herein, detection or determination of behavior(s) can bedifficult. This is particularly true when a behavior can have physical“symptoms” that are similar or even common to other very differentbehaviors. One example is Anger for which physical symptoms may includeraised blood pressure, increased body/skin temperature, and/or raisingof a person's voice. But such measures may also apply to otherconditions or behaviors, such as having too much caffeine (e.g.,elevated blood pressure), being in a very warm environment (e.g., hotbody/skin temperature), talking to someone who is hard-of-hearing (e.g.,raised voice), or talking in a loud environment (e.g., a bar). As aresult, such measures and in particular the associated interpretationsof those measures can vary greatly from person to person.

In order to confirm (or at least increase the likelihood of a correctdiagnosis of) a behavior, exemplary embodiments disclosed herein includea pre-identification of data, measurements, and/or metrics, and in turnthe sensors or other devices that collect(s) and develop(s) such data,measurement(s), and/or metric(s) as they relate to triggers, behaviors,and contexts. Such data, measurements, and/or metrics are taken,measured, and/or detected via sensors, sensor arrays, devices, and/orcommunications networks, including the Internet of Things (IoT) andsocial media networks. Generally (but not a requirement for allembodiments), more than one measurement may be taken, and suchmeasurements may include a combination of one or more physical readingsand one or more “non-physical” readings geared towards fully andaccurately assessing the physical state of a person and even moreimportantly, assessing a person's state of mind and/or mental viewpoint.For example, exemplary embodiments may focus on (loud) word selection(e.g., curse words, etc.) besides or in addition to blood pressure as away of indicating Anger. Other measures might be taken to “calibrate”such readings, such as a sensor that determines if/how much caffeine isin the person's system. Such indicators would be personalized to eachindividual as part of a the “pre-identification” process. For example,Person A may only curse when they are very Angry, whereas Person B maycurse as a normal matter of course (and perhaps only doesn't curse whenAngry).

Even these person-oriented measures (e.g., measures focused on aperson's physical and/or mental state) may not be enough to accuratelydetermine a person's behavior, such that the person's context may alsoneed to be determined via a context identification/determinationprocess. For example, being in the context of an outdoor ball game couldexplain elevated blood pressure (e.g., from excitement), hot body/skintemperature (e.g., outside seating on a hot day in the sun), and evensome (e.g., good natured) cursing (e.g., “you struck out, you jerk!”).Without the context of the hot ball game, the person could easily bemisidentified as being Angry. Thus, identifying behavior accuratelyoften requires a plurality of different types of measurements, e.g.,measurement(s) of the person being monitored and measurement(s) as tothe context the person is in at the time of measurement. In turn,determining the correct course of action to mitigate a (mis)behavior isnot only dependent on an accurate behavior “diagnosis”, it is often alsocritical to have a full (and accurate) understanding of the person'scontext as well. For example, the same behavior of the same person butwith different contexts will very often have very differentpreemptive/risk-mitigating actions. Actions that may be taken will varydepending on the person, behaviors detected, and context, as well as theactions possible depending on support networks, nearby locations, andother factors. Such actions as well as exemplary ways of detecting andinterpreting behavior, context, triggers, and/or actions are addressedand disclosed herein. Exemplary ways of protecting the privacy of thatinformation are also disclosed herein.

Exemplary embodiments may focus on certain kinds of behaviors, contexts,triggers, and actions that enable heretofore new ways of detectingcertain kinds of human interaction (e.g., romantic/sex/sexual-relatedinteraction, fidelity, infidelity, interpersonal relationship, etc.) andenforcing agreements in some form with a variety of pre-identifiedinformation capture, documentation, measurements, compliance metrics,“punishments” or other actions for enforcing such behaviors andassociated agreements.

For example, an exemplary embodiment may be configured to address asituation in which a person (Person A) who wants to ensure that she orhe (“she”) does not undertake sexual activity (e.g., say with Person B(“he”) or any person within type C such as a “just-met” person) when sheis not behaving “normally” (e.g., such as being drunk) and/or when doingso in an unusual context(s) (e.g., anywhere outside her bedroom and whenthere is the potential for her roommates coming home within the next 4hours). Person B may have generally similar concerns, but also specificconcerns about engaging in sexual activity with someone who may laterclaim to “not be responsible for their actions” because of drinking orother factors, exposing Person B to potential accusations of harassmentor worse. Exemplary embodiments disclosed herein enable both Persons tobe comfortable that their sexual encounter “requirements” are being met.Further, exemplary embodiments disclosed herein enable Person A to“certify” or otherwise legally or at least objectively and verifiablyagree that Person A is of sound mind and body and fully agreeing toparticipate in the sexual encounter. Note “sexual encounter” does notnecessarily mean intercourse. Rather, it could pertain to any type ofinteraction between two or more parties, such that some element withrespect to sex is involved, including touching kissing, or otherbehaviors short of intercourse. It would also apply to all forms andvariations of such “romantic activity.” Such agreement(s) would bestored in a form of social agreement and/or potentially even legaldocument, using a variety of secure, and if needed, independentlyverifiable transaction mechanisms including but not limited to securecloud storage, escrow-type data lockboxes, and/or blockchaintransactions. Indeed, such privacy and security mechanisms are importantdue to the nature of the data collected as disclosed herein being highlysensitive or even among the most sensitive possible. Exampleprivacy/security mechanisms are disclosed herein. A security/privacymechanism employed in exemplary embodiments may have a requirementrequiring both/all parties involved to agree to disclose the informationassociated with an agreement or dispute and be precluded technicallyand/or legally from being disclosed without all parties' consent. Inother exemplary embodiments, a single person's agreement or consent mayor may not be all that is needed before disclosure depending on thenature of the agreement.

Furthermore, ensuring the security of the collected data may also helpto avoid disputes with respect to the data collection, including keymeasurements/metrics, places and times, and any interpretations of thedata. Towards that end, the use of blockchain technology (e.g., the useof distributed ledger technologies and methods) may be used in exemplaryembodiments to store collected data in a completely secure, agree-by-allmanner. Accordingly, exemplary embodiments disclosed herein may includeblockchain-based designs for capturing/storing all romantic/sexual“transaction” data as well as ensuring that the data remains privateuntil/unless it is needed. This may include concepts disclosed herein.

As recognized herein, there is a need for a way for one or bothperson(s) (or more than two person(s) depending on the situation) toprotect themselves from false, misleading, or after-the-factregret-based accusations of sexual misconduct through a behaviormonitoring and social/legal contract mechanism.

This is not to paint a picture of reverse power roles with the onlyconcern being for the male and possible false accusations. Both malesand females (biologically or self-identified) will continue to bepresented with situations and circumstances that may tempt or accidentlystumble into types of (sexual) behavior that they are uncomfortable withupon a moment's reflection. But females are physically different thanmen; for example, studies show that women may be more vulnerable toalcohol (such as a 2:1 per drink multiple in terms of impact) even withequal body weight. There are many anecdotes indicating alcohol use byone or both parties in a disputed sexual situation was involved. Studieshave shown or suggest that after drinking alcohol, males may become moreaggressive sexually after drinking alcohol, while women may become moresusceptible to “agreeing” (or at least not disagreeing in the drunkenmale's view) to sex, including in situations where if the woman wassober, she would not have “agreed” to the sexual encounter in anyone'sdefinition of the term. A similar situation from a male perspectivewould be someone who recognizes that he gets aggressive when he drinksand is afraid that he may cross a behavioral and/or socially acceptableline when interacting with others (particularly women). In oneUniversity of Minnesota study, as much as 70% of certain violent crimes(particularly rape) occur when the (male) criminal has been drinking,thus preempting/preventing unacceptable behavior (which clearly rape is,but so are many other behaviors short of rape) by males when drinking isa major objective of this invention, in addition to the obvious benefitsto the (potential) victims of such unacceptable behavior, male orfemale.

As recognized herein, there is a need for a way for a person(particularly those vulnerable to making bad decisions in certainbehavior/context combinations, such as having been drinking with aperson they find attractive) to protect themselves—and others—frommaking a bad decision (or multiple bad decisions). An example baddecision may include drunkenly agreeing to have sex to which agreementwould not have occurred if the person was sober. Another example baddecision may be tending to behave inappropriately when drinking thatputs the person at risk of embarrassment or worse sexually or otherwiseharassing people while under the influence. In today's environment inparticular, such behavior could have life-long lasting implications interms of societal shunning, loss of job, or even incarceration.

As further recognized herein, the benefit from early detection of“trends” towards (presumably “bad” even if intentions are good)behavior, such as having more than one drink in a defined timeframe whenthe person knows that having more in such a time frame has historicallyled to drunkenness. In which case (e.g., bad behavior either having been“achieved” or is going in that direction), exemplary embodimentsdisclosed herein have pre-identified actions at their disposal toprevent/mitigate potential bad decisions. This could, for example,include automatically initiating a messaging series (e.g., text,messaging, voice calls, social media posts) to the person's bestfriend(s) (via preferred interfaces and mechanisms) alerting them to the(impending) bad behavior and providing response(s) as pre-identified bythe person and/or friend(s), along with appropriate methods andinterfaces, pre-identified and selected according the best fit with theperson's behavior, triggers and context. Escalation actions are alsopossible, such that if the friend's “intervention” is not successful, atext or call to the person's mother is automatically placed orthreatened to take place if the person does not alter his/her behaviordirection.

Disclosed herein are exemplary embodiments of systems and methods thatmay address one or more or all of the above needs by using and/orincluding one or more or all of the following.

-   -   Pre-identify behavior (physical and mental actions) and        frame-of-mind of concern to the person (or others), potentially        along with location(s), timeframe(s), situation(s), and        circumstance(s) (context) within or associated with the        particular concerned behavior.    -   Monitoring that behavior(s) through sensors, sensor arrays,        communications networks (e.g., social networks, etc.), devices,        and/or other mechanisms, such as with sensors pre-configured or        otherwise tuned, calibrated, set to, or otherwise focused on        detecting those behavior(s) and (as needed) contexts.        Collectively, behavior (and as appropriate, contexts) may be        referred to herein as “triggers.”    -   After one or more concerned triggers (behavior and if needed        context) is detected (including “trending” behavior/context,        e.g., behavior/context that has not yet reached a certain state        but is moving in that direction), then the system initiates and        manages one or more pre-identified actions to preempt, mitigate,        or (if applicable) “approve” the behavior according to exemplary        embodiments disclosed herein.    -   When preempting/mitigating “bad” triggers is needed, one or more        pre-identified action(s) are initiated and managed by the system        according to exemplary embodiments disclosed herein. For        example, in the case of drunkenness, an alert may be sent to        someone in the person's support network with information about        what is going on and specific (pre-identified) actions to be        undertake by that support network according to exemplary        embodiments disclosed herein.    -   In the case of an “approved” behavior or behavior/context,        exemplary embodiments of this invention may initiate and manage        a process to execute a social/legal contract or otherwise        independent record of consensual agreement between the parties.

Various examples are described below that help to illustrate, elaborate,and describe aspects, elements, and features according to exemplaryembodiments of this disclosure.

Example 1—Person Afraid of Drunken “Yes” to Having Sex

In this first example, a “safety net” may be provided for a person whoknows they are vulnerable to doing, saying, and/or agreeing to someactivity or other “thing” while under the influence of some substance,e.g., give consent that would not otherwise be given while sober or hadonly been indulging below a certain point. A particular example of thisthat can cause great social, legal, and even criminal difficulties isagreeing to having sex or otherwise putting oneself in a vulnerableposition via behavior or activity that may lead to such an end result,such as going bar-hopping with or going home with a person just met, orbecoming (often at the spur-of-the-moment) involved in an activity withthe other person, such as traveling with them, possibly to a unsafe partof town, that may put them in a context that may result in suchdifficulties (e.g., you shouldn't go out in this part of town byyourself—let me give you a ride home). Having (or not) sexual activity(or other romantic activity) under circumstances that could later beinvolved in some sort of dispute after-the-fact is at the core of thisexemplary embodiment.

Pre-identification of acceptable behavior(s) and/or context(s) isincluded as part of this exemplary embodiment (though one-time,multiple, and ongoing adjustment/modification of these behavior(s) andcontext(s) is also enabled herein). For example, a person may want tonever be involved in sexual activity in the following scenarios: A) withsomeone just met; B) with anyone when any alcoholic drink has beenconsumed or other mind-altering substance taken (perhaps with anexception for a “special” someone, discussed more shortly); C) withanyone with whom the person works (with special actions if a possiblesexual partner is a boss, supervisor, other coworker to which the personis subordinate, etc.); D) at certain locations (such as at a parent'shouse), or E) with an ex-boyfriend/girlfriend.

For Scenario B, there may be an exception for a specific person, who isa “friend with benefits.” In which case, that specific person may thenbe placed on a list subject to the other scenario conditions, such asrestrictions on time and place for such “benefits” to take place. ForScenario C, the possible Action Tree (discussed shortly) may have aspecial branch of possible actions (and associated supplemental datacollection) to both “gently” discourage the boss, supervisor, orco-worker and also to collect data for a possible sexual harassmentcharge.

To pre-identify this behavior, the person may have one or more sensorsenabled that are nearby, on, or around the person, such as a bracelet(e.g., see FIG. 2, etc.), a breathalyzer built into their phone thatmeasures blood alcohol content (BAC), and/or other sensors/sensorcombinations on the person (e.g., devices carried by the person,wearables, bracelets, glasses, etc.), in the person (e.g., implants,etc.), and/or near the person (e.g., sensors in the room the person is,on/in the furnishings, etc.) that may indicate a high risk of(potential) drunkenness, such as location in a bar for more than acertain period of time, being in the company of someone that frequentlyor always drinks with the person, the person starts to talk rapidly (ascompared to the particular person's typical talking speed), slurring ofwords, etc. The sensor or other device readings may initially bepre-identified/tailored to the person with subsequent modificationpossible as behavior data is collected andadditions/deletions/modifications/enhancements are determined throughMachine Learning, Artificial Intelligence (AI), Adaptive Learning,Affective Learning, Big Data Analysis, Support Personnel suggestions,pre-identified actions and adjustments, and other mechanisms and methodsfor parameter/configuration adjustments identified. Such technologiesare also used in exemplary embodiments in identifying/implementing keyactions that need to occur/not occur in relation to behaviors and/orcontexts.

The sensors or other devices may be pre-identified and pre-configured todetect the existence of alcohol and/or to detect what degree might theperson have a high intolerance, for example hard liquor. Thus, anydetection of the proximity of hard liquor (e.g., within 2 feet of theperson based on smell, visual evidence, etc.) may be considered as causefor alarm and initiation of preemptive/preventative action(s).

This behavior(s), as indicated by one or more indicators (e.g., badlytrending Blood Alcohol Content (BAC) levels, drinking “friends” present,etc.), and as measured by the one or more sensors that may indicatedrunkenness or an imminent trend towards such, may be pre-identified,and the appropriate settings on the sensor(s) pre-identified and thenpre-configured, such as detecting (and reporting) any BAC of over 0.04%(e.g., the person's view of what the limit is and still retaining fullcontrol of behavior), and/or allowing oneself only to drink at certainestablishments and/or only with certain friends (or definitely not withcertain drinking “friends”). Certain such friends add a non-locationelement to the context setting (along with location), as well asimplicitly introducing the need to sense the particular identities ofcertain (pre-identified) people.

Aspects of this invention enable modification and/or adjustment ofpre-selection and/or pre-configuration of sensors and sensor-settingsbased on accumulation and analysis of historical data and/orobservations of the monitored persons or others. For example, theperson's friends may point out that person starts acting different afteronly 2 drinks, whereas the initial BAC level settings are calibrated for3 drinks. Analysis of the person's drinking data may confirm this, byfor example, walking at a slower, uneven, and/or erratic pace (e.g.,data collected by walking/fitness-type sensors) and/or analysis ofspeech patterns. This may include recording samples of the person'svoice, of which samples are collected when drinking is detected andcompared to non-drinking recorded samples of the person's voice thatwere pre-collected for such analysis purposes. Modification of sensorscan refer to the adding of or deleting of various types of sensors, forexample, adding a blood pressure monitoring capability to an initialsensor set (that did not include monitoring blood pressure monitoringsensor(s)) identified for detecting the Anger trigger, and adjustment ofsensors refers to adjusting the parameters for a given (pre-identifiedor later added) sensor regarding what type/level of data is collectedand when, for example changing the frequency of a periodic measuring ofBAC from 0.04% to 0.05%, and/or changing the frequency of measurementdepending on the BAC level, e.g., once an hour up to 0.04%, once every15 minutes at a BAC level of 0.05%, and continually/near continuallyonce BAC level reaches 0.06%.

When a certain pre-identified drinking threshold is passed (e.g.,0.04%), or a disturbing trend is detected (e.g., going from 0% to 0.03%in 15 minutes), this exemplary embodiment may analyze the possible(pre-identified) actions for that person's behavior and (as applicable)context. Besides the context elements discussed above (e.g., which bar,where located, with what persons), action-related context elements maybe measured and compared to pre-identified concerns. For example,possible actions may vary depending on whether the person drove a car tothe bar or came some other way (e.g., with a friend or an Uber driver).Because this kind of data would not be able to be collected while theperson was in the bar, this exemplary embodiment includes some level of“pre-behavior” data collection of various elements (e.g., driving a car)that may come into play when a behavior concern is activated. Actionscan vary widely and can be individually or in combination. For example,while a certain BAC level may initiate a disabling of the person's careand a call to Uber, the invention may take actions to stop or slow thedegree/volume of drinking, such as sending reminders to stop drinking orplaying drunk-driving-related videos on the person's phone, tocontacting friends, relatives, or other support persons to get them totake actions on behalf of the original person, such as calling them orstopping by the bar that they currently are at. The invention would alsoinclude functionality for the support person they need to respond to theoriginal person's situation, such as the person's location andinformation on how much they've been drinking, who they are with, howlong they have been drinking/at the location, etc.

Although the potential actions may vary, possible actions arepre-identified based on the behaviors (and as applicable, contexts),e.g., “triggers”, and other actions generated by artificial intelligence(AI), “if/then”-type decision trees and/or other machine-basedtechnologies described above. Example actions include sending outmessage(s) to trusted friends or family members; automatically callingan Uber driver (while disabling the person's own car from being driven);essentially forcing the person to leave the bar; and/or sending amessage to the bar/bartender to cut the person off and stop providingany alcoholic beverages to the person. A particular impactful actionmight be a pre-recorded video of that person warning oneself that “YOUARE NOT SOBER—GO HOME!”, perhaps even connecting to Bluetooth speakersnear the person to add a public self-“shaming” element. Other actionscould involve bringing in 3^(rd) parties to the “stopping” action, suchas causing one or both of the parties' mothers to call or be called andput on speaker, creating a Skype/Facetime link, or even transmittingreal-time audio/video of the current situation or a virtualreality/augmented reality simulation of the “direction” that thesituation/context is trending towards. In such circumstances, thesupport network and interfaces chosen to facilitate the actions,particularly communication-intensive actions, are a part of the overallaction selection/generation process as is the action itself.

Determining which interfaces will be most effective in delivering theseactions is important, both pre-identifying and AI-generated interfaces,as will be determining in what context is involved in “delivering” thoseactions (particularly message-based actions). This determining is animportant element of exemplary embodiments disclosed herein, as aremethods and mechanisms for determining and implementing ongoing updatesto those action/messaging pairing as behaviors, actions, and sensorsthat enable monitoring/delivering them are refined and as newtechnologies become available. For example, instead of or in addition toan audio (or even video) recording of the person's self-yelling atoneself, there may be a holographic image instead. Or as implants becomemore widespread, a shock may be delivered to the person in somebehavior/context pairings to avoid public humiliation yet achieving thegoal: to get the person out of the predicament the person is about toget into before it is too late. By way of example, an exemplaryembodiment may use an electrode-based vibration-inducingsmartphone-controlled system, such as Quell, etc. An aspect of theinvention will be deciding which kind(s) of interface(s) will be mosteffective given the person's profile as well as location and context.For example, the interface used if the person is driving will bedifferent than if in a quiet place and different than if the person isin a loud bar or a crowded ball game.

Example 2—Person Afraid of Drunken/Inappropriate Behavior

This second example is the converse of the first example describedimmediately above. This second example addresses the scenario in which aperson is afraid of being accused of inappropriate behavior, such asdrunken sexual assault, and understandably wanting to prevent that fromhappening before it happens. The challenge, in today's society inparticular, is that one person's well-meaning but “forward” oraggressive behavior may be another person's sexual harassment or worse.

There are two main windows of concern addressed by this exemplaryembodiment. The first is while the “harassment” is actually happening,e.g., Person A (sometimes under the influence) tries to convince PersonB (also possibly under the influence) to have sex. Person A may be tooforward, such as by putting hands in inappropriate places, sayinginappropriate things, or talking too suggestively, to make this happen.Or, Person A might only say a few things to gauge Person B's interest inan encounter, none of them particularly objectionable, at least toPerson A. Regardless, Person B may become offended (which may be visibleor not), and who finally says no and leaves whatever context/setting atwhich they were together.

Historically, that might have been the end of a dud date perhaps a bitof an unpleasant encounter, and that would be that. But more recently,social media allows for this unpleasant encounter to be “reported to”dozens even thousands of followers, who can quickly start gunning forPerson A's reputation, job, etc. Often found in newspapers are storiesin which Person A is fired from their job and/or their reputation ismined without any due process or even proof other than the accuser'saccusatory words. This exemplary embodiment may provide such proof toprotect oneself by capturing by various types of sensor measurements orother data collection mechanisms (e.g., audio, video) at key times invarious forms, e.g., key “snapshots” of the evening that can be used torefute (or confirm) the behavior that took place.

An even more problematic scenario is when Person A is “successful” withPerson B. For example, Person A convinces Person B to (seeminglywillingly) go to an apartment and (seemingly willingly) have sex. PersonB then leaves or sleeps over and returns home in the morning.Historically, when this would happen, if either party has regrets aboutwhat they did the night before, they chock it up to people blinded bythe throws of passion and perhaps alcohol and resolve not to do itagain. But current society has introduced new variations in thisMonday-morning quarterbacking. Instead of writing the evening off bysaying “I'll never drink that much again”, or “I have to stop doingthis”, the “aggrieved” person may feel that someone has to “pay” for“making” Person B have sex now that Person B is having the morning-afterregrets. Another scenario might be that there is no “true” issueinvolved, but Person B, who lives at home, is caught by the parents. Ina panic, Person B makes up a cover story that implicates or accusesPerson A in harassing behavior or worse. In either such examples, PersonB may talk to friends, take to social media, and/or even call thepolice. Person B (or his/her parents or friends) may contend that sincePerson B was not fully under control of their own faculties (e.g.,because Person B was drinking), then what happened was sexual harassmentor rape because Person A took advantage of Person B. As noted, sometimesthis is “crime” is “caused” by a 3^(rd) party finding out about theencounter, such as a parent or supposedly close friend who blabs to theworld, embarrassing Person B and “forcing” Person B to manufacture a“defense” by claiming that Person B was taken advantage of even if itwas mutually and consensually agreed to and no matter the consequencesto the other party.

Person A in this situation often has no evidence other than their word,and perhaps a few friends or acquaintances at the bar who could testifya “subjective” opinion as to how drunk/control of their faculties PersonB really was at the time. Seemingly, this type of accusation in recenttimes has the accused as guilty until proven innocent and sometimes noteven then when it comes to Person A's job and reputation in society.

Accordingly, recognized herein is a need for and discloses hereinexemplary embodiments of systems and methods to monitor behavior (byboth parties); and preempt the sexual “transaction” from going too farif various warning signs/risks are detected. If the transaction proceedsanyway, the collection of data as disclosed herein may protect bothparties from an after-the-fact accusation of harassment, rape, etc.

Example 3—Woman and Man Want to Ensure Genuine “Yes”

This third example address a scenario different from the first andsecond examples described immediately above. In this exemplaryembodiment, Person A wants to say “Yes” and is in the rightstate-of-mind (e.g., behavior) and context (e.g., safe place). Person Balso wants to say “Yes” and is in the right state of mind (e.g.,behavior) by being mentally genuine about a Yes (e.g., not being drunk)and in the right context (e.g., also a safe place).

This exemplary embodiment includes monitoring both parties' behavior andcontext and comparing the monitored behavior and context to a list ofrules/preconditions. If those rules/preconditions are met and there areno major red flags in terms of “executing” (such as Person B having nomoney to get a ride home in the morning), then alerts may be provided toboth of them that the “coast is clear” regarding getting together. Atthat point, a social contract (e.g., legally and/or binding contractand/or mutually acceptable social agreement, etc.) is presented to bothparties to attest that they are both in the right frame of mind andbody. The parties may then click the appropriate button, or in someother form (e.g., body language, etc.) accept and agree to the socialcontract. Accordingly, the parties have now insulated themselves fromany sort of morning-after regret and, worse, actions that couldwrongfully accuse one or the other party of taking advantage of theother.

Example 4A—A Full Agreement to Have Sex and the Nature of the Sex

If there are no conditions, exceptions, or other identified inhibitorsregarding the participants having sex, then a contract (social and/orlegal) or some other type of formalized agreement is provided for theparties. This fourth example 4A includes not just information about theconsensus of “yes”, but if needed agreement on time, place, duration,and range and/or type(s) of sexual activity. The latter may be somewhatproblematic both in terms of diminishing the spontaneity of the moment,but also not knowing what types of sex both parties are mutuallyinterested in. That said, some sort of documentation or other recordedproof may be provided—even if only a brief yes/no to prompted questionsfrom one party or even a prompting from the system regarding differenttypes of and ranges of sex encouraged, allowed, off limits, areas of thebody that can be touched and how, any safety words, etc. If agreed to byboth parties (through a verbal, video, or other affirmation mechanism),a record of the sexual occurrence will be taken. This can also be doneby various means, utilizing both parties' devices and/or other sensorsin the room/area/bed etc. where the occurrence occurs, particularly (butnot limited to) video and/or audio recordings.

If a forbidden act or act not explicitly agreed to is detected(typically verbal or physical cues or through pre-defined recognitionalgorithms), the device (of one or both of the parties) or otheralert-capable sensors may then document the “termination” of the act aswell as the aftermath. If the act continues or continues in anon-approved way, appropriate alerts or other actions are then taken todiscontinue the actions.

Example 4B—Retraction of a Yes

There are circumstances where after an agreement has been made and sexhas commenced, one of the parties may retract or revoke consent andchange their mind about the sexual encounter. This can be veryproblematic as cues given to indicate the revoked consent may not beobvious (e.g., “STOP!”) and/or the other party may be wrapped up in theactivity and be oblivious to the change of mind, incapable of correctlyinterpreting the cues, or just plain refuses to listen or doesn'tbelieve it (e.g., No doesn't really mean NO). The technology of thisexemplary embodiment 4B can be helpful in such situations aspre-identification of cues may be captured by the system according tothis exemplary embodiment. “Safety words” or sounds (e.g., clapping ofhands three times) may be used to trigger the system to take action(s).The system action(s) may be tiered, such as warnings to Person B:“Person B—Stop Please” escalating to Loud Alarms, calling 911 in extremesituations, etc.

Example 5—Woman/Man Afraid of Post-Sex Accusations

This fifth example is a variation of the above examples and isapplicable to “morning-after” regrets. Unfortunately, it can bedevastating on a person who thought everything was consensual the nightbefore to find out the next day that the other party believes otherwise,e.g., the other part is not content to chalk up their consent at thetime to foolishness or other reasons they alone are responsible for,decides to blame the other party, etc. Or, the person involved is acelebrity and wants to make sure they are not being used. This exemplaryembodiment looks to preempt this by “documenting” the consent andsubsequent actions (potentially even the romantic encounter itself withconsent) in the form of a contract (e.g., verbally “signed” legalcontract, etc.), e.g., an immediate post-romantic encounter “agreement”(e.g., verbally, video, etc.) that the encounter was consensual alongwith readings of the physical state of both parties if possible (e.g.,sound mind and body). The combination of the pre-encounter agreementrecord and the post-romantic encounter agreement should dissuade theother party from a next day “revision” of the consensual encounter to anon-consensual encounter and/or provide the accused with irrefutableproof regarding the consensual nature of the encounter.

This exemplary embodiment may also include monitoring of the potentialreviser/revisionist/accuser's social media and other communicationsnetwork accounts that would potentially signal a change of heart if itoccurs over time (particularly if the romantic encounter does not extenda relationship or itself was unpleasant in some way). It could alsoprovide non-testing proof of paternity (e.g., a “time-stamp”) of theencounter if needed at a later date. Various mechanisms (e.g.,machine-based, etc.) focused on key words may be used to monitor thesocial media accounts of the other person (and possibly his/herfriend(s)), with alerts or other actions triggered when achange-of-heart is detected or trending towards a change-of-heart.

Example 6—Post Interaction Behavior Monitoring (Both Parties)—IncludingSocial Media

This sixth example includes a variation of part of the fifth exampledescribed immediately above. This embodiment provides a trolling/botreview and analysis function of all of the parties' social mediaaccounts to detect/determine if unpleasant posts are appearing or ifsuch posts are “trending downwards.” This exemplary embodiment includesa “scoring” of sorts regarding each post and provides an alert if a postgoes below a certain predetermined negativity threshold regarding one ofthe parties or if trending downwards.

Example 7—Falsehood Monitoring

This seventh example is a variation of the fifth and sixth examplesdescribed immediately above. This exemplary embodiment includes socialmedia monitoring capability specifically looking for falsehoods,particularly with respect to the romantic encounter. Upon identificationof potential falsehoods (e.g., libel, slander, etc.), notifications aresent to the victim (e.g., directly to the victim and/or to the victim'slegal representation, etc.) along with a cease-and-desist order (e.g.,via social media and/or legal form, etc.).

Example 8—Underage—Adult Inappropriate Interactions

This eighth example may address inappropriate and illegal sexualliaisons between persons under legal age and those of legal age. Avariation of this is when both persons are of legal age but given thelegal structure of the relationship (e.g., teacher-student), a sexualrelationship is prohibited.

Upon a physical or virtual interaction commencing between two suchpersons, and a concern is raised by one (for example, the adult hasconcern about the other party's age, but the underage party says theyare of legal age), exemplary embodiment 8A will “activate” a datacollection process to collect as much information as possible on bothparties. This includes taking “measurements” of the persons such asphotographs, voice recordings (including assertions of legal age by thepossible underage person), etc. and essentially performing a real-timeprivate investigation of that person. A voice recording givingpermission may be captured. Various databases may be scrutinized with afocus on detecting age-specific information, including birthdate, schoolattendance (with associated extrapolation of birthdate), etc. If suchaccess is blocked due to minor privacy restrictions, this exemplaryembodiment considers that as an indicator of being underage. Any suchindicators may then be reported to the adult person and with additionaldata capture (e.g., voice recordings, video, etc.) that will provide arecord of the adult being informed of the situation along with(presumably) a record of the social interaction being terminated.

A variation of the recording of the interaction between the adult andunderage person will take place with some differences in anotherexemplary embodiment 8B. Because a pre-existing relationship (e.g.,student-teacher) is already acknowledged in this example, this exemplaryembodiment focuses on detecting unusual or “out-of-bounds” behavior oneither participant. For example, a sensor may be activated when bothpersons are within proximity of each other and another “keyword” sensormay be calibrated to detect any untoward interaction, such as words“sex”, “come back later”, “meet me at”, and so forth. If such key wordsare detected, additional sensors are activated (e.g., video, full-timerecording, etc.) to capture a complete record of the interaction withforwarding of the interaction later or in real-time to the appropriateperson (e.g., parents, school administrators, authorities, etc.).Actions may also be triggered such as messages telling the underageperson to come home or otherwise remove oneself from the situation. Ifthe “aggressive action” is by the underage person (e.g., a high schoolsenior male, etc.), additional action(s) may be taken to protect theteacher or other party, such as a real-time alert to a school principalor other school personnel to come immediately to the classroom orlocation where the interaction is taking place.

Example 9—Sexual Harassment “Scoring”

Many forms of possible sexual harassment are not well defined and may beto a degree in the eye of the beholder, cf. how an alleged harasserinterprets a comment, gesture, or action versus a recipient'sinterpretation. Historically, verbal-only differences have notconsistently been considered a major deal at least for individualoccurrences and do not lend themselves to requiring a solution.

But any accusation of harassment in any form (even if unfounded orwithout any specifics) may be sufficient to damage and even end a careerand/or ostracize a person in society. This exemplary embodiment includespre-identification of behavior(s) and context(s) that may lead toinappropriate content generation or other types of posts with the sameoutcome; includes (e.g., puts in place, etc.) sensors and othermechanisms to detect as soon as possible a trend (hence, a scoringmechanism) towards such behavior, context, and associated social mediaaction; and takes action(s) to stop the posting of the content, modifythe content, and/or redirect the content to other platforms where itwill have less impact.

For example, Person A may have an issue with what Person A considershypocritical political postings on Twitter. Person A's “problem” may getconsiderably worse when Person A knows or has met people with thosepostings, and furthermore if those postings have to do withsexual-related disparities and perceived “wrongs” done by Person A's“culture” (such as being a white male) against Person B's culture (beinga minority female). Person A may recognize this propensity to get Angrywhen faced with what Person A views as unfair, biased, or unreasonableposts, and would appreciate a form of “safety net” provided by thesystem to protect Person A against oneself, and thus prevent or limitthe types of responses Person A does when exposed to such provocativepostings. Person A may also want to keep a record of the transactionsinvolved such that if there is any after-the-fact question of whetherharassment or other inappropriate behavior took place between Person Aand Person B, then there is a record that can be consulted, thusavoiding a he-said/she-said situation; one that may be skewed harshlyagainst the one accused of harassment to the extent that they can losetheir jobs and livelihood without any sort of due process.

The above examples 1-9 are generally or specifically addressed atromantic/sexual embodiments in some form. The following examples alsoutilize technology/methods described in previous embodiments but areless romantic/sexual in nature.

Example 10—Social Media Self-Protection

It has become relatively easy to literally ruin one's life with anoff-color, politically incorrect, inflammatory, and/or otherwise stupidcomment or post on any number of apps such as Facebook, Instagram,Twitter, other social media, etc. This exemplary embodiment looks topreempt such unwise remarks/postings by pre-identifying those types ofremarks/postings that a person is susceptible towards (e.g., politicalpostings, etc.), and in particularly pre-identifying the behavioralconditions and context that are at the greatest risk for postinginappropriate content and then monitoring the person and theircommunications/communications channels.

This exemplary embodiment focuses on proactively identifying theTriggers, Behaviors and/or Contexts when such communications/postingoccur and proactively/preempting such communications/postings throughone or more actions. For example, when such conditions occurred, thisexemplary embodiment may enact a variety of actions ranging fromtemporarily disabling a Twitter account, enacting a social media“overlay” that will allow a post to be written, yet delaying its actualposting (e.g., similar to live TV seven-second delay) to immediatelyaddressing the trigger(s) such as “soothing” the triggers(s) (such asAnger) that make the person more susceptible to posting inappropriatecontent in the first place.

A variation on the above is a preempting of communications regardingcertain topic(s), such as topics that fall under the old adage of nevertalk “sex, politics, and religion in polite company”. With a heightenedfocus on “micro-aggressions”, a proactive form of self-censorship mayhelp to avoid antagonizing one of many identity groups. This exemplaryembodiment facilitates that by detecting Trigger, Behavior, and/orContext(s) that a person might be susceptible to saying (posting orotherwise communicating to the outside world) such antagonizingstatements. Once pre-identified (again, with Anger a high potentialtrigger), one or more actions may take place focused on preventing ordelaying such postings/communications and/or mitigating the damage bysoothing or otherwise addressing the Trigger. Post-communicationsaction(s) may also take place, such as automatic deletions of postingsto prevent or at least reduce the number of persons viewing thepostings.

Example 11—Bullying

This eleventh example is a variation of several above examples and inparticular the previous tenth example described immediately above. Thisexemplary embodiment is focused on both preventing bullying of aparticular person by pre-identifying triggers, behaviors, and/orcontexts when he/she is susceptible to performing bullying (andpreempting/preventing it), as well as detecting bullying by others andpreventing a response (likely in reaction to a trigger being activatedby the bullying). Besides the monitoring for and detecting/determiningpre-identified triggers, behaviors and as applicable contexts asdisclosed herein, these exemplary embodiments also includepre-identification of known likely bullies/persons to be bullied andmonitoring their locations (physical and virtual). When proximity isdetected (e.g., in a social media context in which they are online oractively posting, etc.), this exemplary embodiment includes takingsteps/actions to essentially prevent the parties from being aware oftheir respective social media or other activity, and thereby preventinga response.

Example 12—Domestic Violence Accusation; Restraining Order

This twelfth example focuses on prisoner/parolee/probation situationsand with domestic violence-related embodiments focused on preventingphysical contact. This exemplary embodiment elaborates on the capabilityand extends it to “virtual” contact, which may or may not be construedby the court as a violation of a restraining order. But virtual activityby an accused/criminal may be considered a threat by the victim, andthus should be prevented. In this exemplary embodiment, anaccused/criminal's behavior, trigger, and/or context(s) are monitored todetect those that might “cause” the criminal/accused to post threateningor otherwise uncomfortable communication, social media posting, etc.that may be construed negatively by the court. When suchtriggers/contexts/behaviors are detected, then similar to exemplaryembodiments disclosed above, this exemplary embodiment may initiate,implement, or take action(s) to relatively immediatelypreempt/prevent/mitigate such communications/postings. Upon thedetection of such triggers or high risk of such triggers, action(s) maybe initiated to the accused/criminal's support network to immediatelyaddress the “near-miss” and provide support to prevent any suchtrigger/behavior/context from happening again, if possible.

Example 13—Workplace/Co-Worker Behavior “Compliance”

There is some anecdotal evidence that suggests that men are becomingreluctant to meet singly with a woman whether on work premises oroffsite and/or even communicate with a woman when there is no way ofverifying the integrity/accuracy of the communications. Evidence hasstarted to indicate a reluctance of even hiring women under a fear of a“guilty until proven innocent” atmosphere where even a single,uncorroborated accusation may be enough to destroy a man's reputation,lose a job, and/or lose prospects of future employment.

To prevent (or at least reduce the occurrence) of such situations, andmore positively possibly help in restoring trust in the workplace, thisexemplary embodiment seeks to capture relevant information aboutworkplace interactions particularly those involving one-on-oneinteractions between a man and a woman and more broadly a person havingauthority or power over another person in a subordinate position. Assuch, this exemplary embodiment will detect or otherwise initiatemonitoring of behavior when “high-risk” situations appear, such as aone-on-one meeting between a boss and a subordinate in the boss' privateoffice (e.g., detection of a high-risk context). Upon such detection,monitoring/data collection of various types as described above commenceswith a focus on monitoring and, if needed, documenting the behavior ofthe boss or other person of power/authority, as well as thecircumstances leading up to a determination of the high-risk situation,such as suggestive comments and/or body language of one or both parties.Upon such detection, actions may then be initiated to prevent or atleast reduce the possibility of a potentially uncomfortable (or worse)situation from progressing, such as initiating alerts, requesting otherparties to “intrude” on the situation, and/or other explicit actionsdeemed to interrupt or otherwise “correct” the situation.

Example 14—Adultery/Suspicion of Adultery/Proof of No-Adultery

A variation of several of the above examples utilizes aspects to “prove”adultery or no adultery through monitoring of and behavior detection ofsuspected party(s) involved. In this exemplary embodiment, themonitoring may be performed on a voluntary or involuntary basis (orboth). This exemplary embodiment focuses on beginning the monitoringwhen “suspicious” behavior begins, such as being in strangelocation/contexts at unusual times and/or exhibiting certainbehaviors/Triggers outside the norm (for that person(s)). Detection ofsuch context/behaviors/triggers activate certain action(s) that mayrange from “discouraging” adultery-leading behavior to intensemonitoring of locations/activities/behaviors, which may includecapturing data of any romantic/sexual liaisons that could be used insubsequent divorce proceedings.

Other related embodiments are not restricted to permissions/permissiblebehavior as it is related directly to sex or other forms of “commercial”harassment. There are other permissible behaviors that may be deemedmonitorable and turn actionable such as job interviews, educationinteractions, private investigations, etc.

Exemplary embodiments disclosed herein may advantageously provideindividuals with a way to ensure there is no misunderstanding about whatactivity(ies) each party is agreeing to that is accessible, timely, andflexible. As disclosed herein, exemplary embodiments may advantageouslyprovide a way to capture consent as well as a lack of consent. Thetechnologies disclosed herein enable or provide guardrails for ongoingconsent. By way of example, an exemplary embodiment includes an app(e.g., Apple iOS app, Android app, etc.) that includes features such asemail login, a customized icon, ability to create a personal profile,etc. The app also includes expanded capabilities, such as connection toa server (e.g., a company or third-party server, etc.) to store consentdetails, ability to set permissions to integrate with wearables and toaccess to a smartphone microphone and camera that are delivered in aninterface. Continuing with this example, two versions of the app may beavailable. The first app version may be free with functionality for anin-app purchase to capture a one-time “yes” or “no” choice for anactivity, e.g., yes for consent or no for lack of consent tointercourse, oral sex, anal sex, other activity, etc. Space fordisplaying ads via the app may be available to provide additionalrevenue. The second app version may be a subscription version thatincludes expanded capabilities for monitoring ongoing consent.

Some exemplary embodiments disclosed herein may be generally dividedinto three main aspects or parts: (1) preemption, (2) agreement/contractformation, and (3) ongoing monitoring and ongoing validation to ensurecompliance or detect violation(s).

According to the first preemption aspect, a preemption mechanism isgeared to making sure that the participants really want to progress tosome level of interaction or activity such as by meeting predeterminedconditions (e.g., via comparing to acceptable/nonacceptableconditions/triggers/contexts/behaviors) listed in their profiles. To theextent that the profile does not seem to capture the user's desires on aparticular condition, etc., the system may be configured to dynamicallycreate such a condition, etc. based on the specific situational data.The system may also be configured to include or issue a warning or alertfor the user indicating that this an unanticipated condition, etc., andthat the system-created condition is a system recommendation only forwhich acceptance, rejection, or revisions is needed from the user.

For example, a person may meet another person at a mutual friend'swedding at which alcohol is involved. While his/her profile may includethe condition or stipulation about not meeting people when drinkingalcohol, this may be varied depending on the situation. Or, it may be ablanket no-alcohol restriction, but there may be a separate “meet peoplemy friends also know” parameter. In which case, these 2 profile elements“conflict” to some extent relative to the wedding scenario. Thus, thesystem may then make recommendations for Terms and Conditions for thatparticular interaction. For example, the system may be configured toweight and compare individual parameters and make a recommendation basedon highest weight parameter. In this example, if he/she has no alcoholas Very High in importance and “meet people my friends know” as just aMedium importance, then the system recommendation would be “No.” But theuser may want to override the system recommendation. In which case, thesystem may not allow the user override without first requiring that afriend “vouch” (e.g., a third-party voucher) for the potential mate,which vouching may be a general default requirement in some cases. Thisvouching requirement may be implemented via an online vouching signaturecapability within an App (e.g., Apple iOS app, Android app), via a voicerecording, or via another method that would confirm the friend'svouching for the “transaction.”

Continuing with the preemption aspect for this wedding example, a listof actions may be provided to help the user avoid or dodge an evolving,and perhaps confusing, situation. For example, the list of actions mayinclude texting a friend of the user that is pre-identified and listedas a support person to the user. The friend may be known to also be atthe wedding if the friend's location is being individually monitored andtracked and/or if all of the user's pre-identified support persons arepinged for their respective locations to determine which supportperson(s) are at the wedding if it becomes known that a situationrequiring a friend vouching has arisen. Thus, this action functionalityto preempt/avoid a situation from going further is an important aspectof the overall preemption functionality in exemplary embodiments.

According to the second aspect, this assumes that above hurdles havebeen cleared, and it's time for agreement/contract formation. This wouldinvolve the parties (e.g., two or more persons, etc.) to agree that somesort of romantic/sexual/interpersonal interaction or activity isacceptable for the parties involved. In exemplary embodiments, an Appmay be used that is on just one person's smartphone or other device orthe App may be on the smart phone or device of each party or participantto be involved with the interaction or activity. In some exemplaryembodiments, the friend or other third-party voucher may be provided byan App on the smartphone or other device of the vouching person or oneof the parties to the agreement/contract.

The agreement/contract include terms and conditions, and importantlyagreement on the ability for monitoring behavior of the parties aftercompletion and execution of the agreement/contract to ensure complianceand non-violation. The agreement/contract may include static terms andconditions that are the same in every case (e.g., boilerplate legalese,etc.). The agreement/contract also includes dynamic data about thepersons involved and the situation/context involved. Theagreement/contract would include information about how far aninteraction is allowed to progress and in what form. This may includeidentification of behavior(s) that are acceptable and behavior(s) thatare not acceptable, and information about how the acceptability ofbehavior(s) might vary by time and other circumstance(s).

For example, it may be the (somewhat drunken) user is ok (with a friendvouching, either overall or perhaps even by specificbehavior-by-behavior) to allow the other party to only progress to acertain limited level of sex-related behavior as defined in the termsand conditions the first encounter/date and the next twoencounters/dates, if any, occur. This would thus include ongoingtracking of both parties. And if there is a fourth encounter/date, thenthe user may allow sexual intercourse or higher level of sexualrelations, e.g., perhaps without conditions and/or overriding the userprofile, according to the general conditions or a “custom” set ofconditions (e.g., depending on location, etc.). The terms and conditionsmay have “custom” conditions related to monitoring/contact verificationas explained below for the third aspect. This may include the ability tomonitor one or both/all party(ies) social media account(s) to ensure,for example, that there is no disclosure of their relationship in anyway unless explicitly agreed to (later) by the parties. Or if disclosureof the relationship is allowed, this may include the ability to monitorone or both/all party(ies) social media account(s) to ensure there is nodisparaging comments or other disclosures about either party as definedin the terms and conditions.

The imminent interaction of the parties and the ongoingmonitoring/validating may occur dynamically in exemplary embodiments.Because the preemption aspect described above may also be dynamic (e.g.,creation of custom, on-the-fly conditions because of conflicting profileparameters, etc.), exemplary embodiments may therefore include featuresthat function, operate, and/or change dynamically in relation to thecontext and other parameters of the situation, such as anticipating,reacting, recommending, monitoring, and alerting with respect to(un)desired behavior(s). The contracting functionality disclosed hereinmay include custom/dynamic evaluation/creation of modifiedacceptable/not-acceptable situations, etc., third party/friend vouchingcomponents, customization by-type-of-romantic-behavior (e.g., 3′¹ base,type of sexual activity, etc.) allowed and dynamically inserted into theagreement/contract.

According to the third aspect, ongoing monitoring and ongoing validationis performed to determine whether the agreement/contract has or has notbeen violated, including detecting violation(s) and initiating action(s)to address the detected violation(s). Accordingly, this third aspectrelates to functionality that happens after the agreement/contractformation. This includes tracking behavior of the parties (two or moreindividuals) relative to each other and relative to thecontracted/agreed upon behavior. This behavior may practically beanything but will be customized based on the contract/agreement. Thismay start with monitoring immediate “intimate” behavior using one ormore devices or sensors as needed (e.g., video, audio, heart rate,etc.). For example, two or more sensors may be used to confirm theoccurrence of a given behavior. A plurality of sensors may be used tomonitor location and related context at the location (e.g., only at myapartment, only on a weekend when I haven't had more than three beers,only if we just got home from a baseball game, etc.) and relatedbehavior (e.g., only to 3rd base for the 1st interaction/date, etc.).The sensors for monitoring the location, context, and behavior mayinclude social media monitoring. The type of behaviors and limitationsassociated with each behavior should requirement agreement by both/allparties, e.g., if initially proposed by only one party/user andnegotiated thereafter by the parties/users. Based on the sensor output(e.g., detection of safety word(s), gestures, other detectable actionsby a person, etc.), alert(s) may be provided and/or other action(s)initiated, e.g., a set of actions initiated such as loudspeakerwarnings, texts to friends, contacting police, etc.

The ongoing (post-interaction) monitoring may be focused on differenttypes of behaviors at different times, cf, monitoring during theinteraction. The post-type behaviors may focus on mutual behaviormonitoring of both/all parties. For example, if Person 1 had fullyconsented (with verification/legal agreement), then Person 2 may want toensure that Person 1 does not disparage Person 2, e.g., by social mediaposting damaging to Person's 2 reputation. For example, monitoring toolsmay be implemented and integrated with an App to warn Person 2 ofdetected disparaging social media posting(s) by Person 1. Conversely, ifPerson 2 discloses the relationship with Person 1 in violation ofPerson's 1 demand that there be no disclosure of their relationship,then Person 1 may be alerted and provided with the details of adetection that Person 2 has disclosed the relationship. The system maybe configured to provide recommend action(s) for one or both/all partiesto take in either of these above example scenarios, such as notifyingthe violator about violating the terms of their agreement and tocease-and-desist.

Exemplary embodiments disclosed herein may be used for the preemptionand/or detection of an extra-relationship affair or cheating, e.g.,extramarital cheating/affairs by spouses, boyfriend/girlfriendextra-relationship affair/cheating, etc. In addition, exemplaryembodiments disclosed herein may also be used to detect “upstream”behavior or indicators that are likely to indicate a futureoccurrence(s) of an extra-relationship affair or cheating. Suchexemplary embodiments are not limited to detection of the actualoccurrence of the extra-relationship affair or cheating, but may alsoinclude monitoring, tracking, and/or detecting behavior(s) that mightlead to and/or that may be predictive of a future occurrence of anextra-relationship affair or cheating.

Regarding detection of an extra-relationship affair/cheating and/ordetection of breaking or violating an agreement in a romantic encountercontract, exemplary embodiments are not exclusively focused ondirect/obvious indicators of misbehaving, cheating, agreement/contractviolations, etc. as such exemplary embodiments also focus on earlyindicator(s) predictive of such behavior. For example, in the case ofextra-relationship affairs/cheating, an early indicator may includecreation of a user account on a website that facilitates finding awilling participant for an extramarital affair, etc. As another example,an early indicator may include tracking of a spouse to a location (e.g.,bar after work, etc.) more frequently than historically. In addition,other example early indicators of an extra-relationship affair/cheatingmay include triggers as disclosed herein, such as the triggersassociated with loneliness, depression, unhappiness with a spouse orother partner, etc.

Accordingly, exemplary embodiments may detectpossible/trending-in-the-direction-of an extra-relationshipaffair/cheating and then initiate preemptive action(s) to attempt toprevent or reduce the likelihood of the occurrence of theextra-relationship affair/cheating. For example, an alert may beprovided to a husband in that process of setting up a user account on awebsite that facilitates finding a willing participant for anextramarital affair that his wife will be notified (but has not yet beennotified) if the process continues and the husband's user account isfully set up and activated. Exemplary embodiments may allow a person ina relationship for which the other person is suspicious (but yetunfounded in reality) to willingly signup for monitoring in order toappease that other person. Example preemptive actions may also includesuggest marriage or relationship counseling or some other activity thatwill help the spouse or other partner come home from work in a muchbetter mood.

With exemplary embodiments disclosed herein, an affair may possibly bestopped before it started. Or, exemplary embodiments disclosed hereinmay allow for an agreement to be created between persons that stipulatedthat they were having an affair and stipulated what kind ofactivity(ies) is permissible and what kind of activity(ies) isprohibited (e.g., different types of intimacy, sexual activity(ies),etc.). The existence of such an agreement, the parties would thenprovide protection against unfounded allegations of prohibitedactivity(ies) (e.g., different type(s) that are set forth in theagreement as permissible. Accordingly, exemplary embodiments may allowfor (a) avoiding misunderstandings as to which activity(ies) areprohibited and which are permissible, (b) preempting or stopping aprohibited activity(ies) before it proceeds to far, and/or c) provideone side (or the other) with definitive legal and/or behavior trackingsupport for what did (or did not) actually happen.

Exemplary embodiments disclosed herein may also prove useful for controlfreaks, overly jealous persons, etc. For example, a person beingcontrolling and becoming a control freak does not happen instanteouslybut instead involves a process that happens incrementally over time.Thus, exemplary embodiments disclosed herein may provide the ability todetect controlling behavior relatively early. This early detection andimplemented remedial action(s) thereafter may help the controllee aswell as informing someone who might not be self aware that he/she isengaging in or exhibiting controlling-type behavior(s), which if leftunchecked/unabated may min a relationship. This avoidance of thecontrolling/jealous-type behavior may be implemented at various stagesor processes in exemplary embodiments, such as in someone's profile, in“screening” a person before moving into dating/intimacy mode, todetecting controlling/jealous-type behavior “the morning after” as wellas in established relationships including marriage, etc.

Like other undesirable behavior, there are a variety of actions that mayimplemented or initiated, including action(s) that are upstream of andtaken before the undesirable behavior after early detection of issue(s).For example, the early detection may include early detection of trendseither in frequency of controlling/jealous-type behavior(s) and/orseverity of the behavior and/or types of behaviors. The actions, inturn, may include notifying both or all parties of the detected trendand suggesting actions to reduce or preempt the frequency, severity,and/or type(s) of the controlling/jealous-type behavior(s) in thefuture.

For example, if there is an increase in money arguments between spouses,an exemplary embodiment may include recommending that parties to agreeon a discretionary budget such as a discretionary budget based on an AIanalysis of the financial and purchasing habit parts of their profilesand/or to modify/build a person's financial/purchasing profiles andhabits based on a plurality of sensors' observations, readings,measurements, etc. taken over time. A recommended budget may include amonthly dollar value limit to spend on clothes each month where spendingis monitored to detect when that limit is being approached. Such aspending limit would not necessarily be an outright victory for thecontroller, as the controller may have been demanding that no money isspend and instead of a budgetary limit for clothing purchases eachmonth. For just-met or other early-dating couples, suchdemands/arguments about money may be more troubling as it spendinghabits may be too sensitive or private at the start of a relationship.In which case, more emphasis may be placed on “extracting” the personfrom the relationship or at least informing person(s) within thecontrollee's support network (e.g., alerting loved ones of thecontrolled person with the controlled person's permission, etc.) aboutthe troubling control freak tendencies emerging in the new relationshipaccording to an exemplary embodiment. The controlling-typeperson/control freak in an early stage of a relationship may be alertedabout the undesirable behavior, e.g., advised to stop the behavior andseek counseling, etc. according to an exemplary embodiment. Accordingly,this exemplary embodiment may be operable as a virtual counselor forboth parties when dealing with incremental behavior (e.g., indicators ofcontrolling or jealous behavior(s), etc.) that if leftunchecked/unabated may escalate from being a minor nuisance(s) to amajor issue(s) and/or relationship trigger point(s) in the future.

Embodiments disclosed above involve parolee orpersons-under-restrictions and include novel and inventive capabilitiesas do the exemplary embodiments disclosed hereinafter that involve aperson(s) under quarantine. The example discussed next involves aperson(s) under quarantine being allowed (under certain conditions) togo to a grocery store, which may be considered similarly to, essentiallythe same as, or substantially no different than the concept or detailsfrom, for example, a parolee under house arrest who might be allowed(under very controlled conditions) to leave his home to see his doctor(or go the grocery store or similar necessary/benign purpose).

The term “quarantine” has a number of definitions. As a noun, quarantinemay be defined as a state, period, or place of isolation in which peopleor animals that have arrived from elsewhere or been exposed toinfectious or contagious disease are placed. As a verb, quarantine maybe defined as impose isolation on (a person, animal, or place); put inquarantine. Exemplary embodiments may utilize these definitions andother definitions including “self” isolation or quarantine, such as aperson having been exposed to a virus/illness and takes the precautionof self-imposing on themselves a predetermined quarantine/isolationperiod (e.g., a 14-day quarantine/isolation period, etc.). It could alsoapply to embodiments involving refugee containment/management, animalcontrol (e.g., not relegated to humans only, particularly given thatmany diseases have their “start” in non-humans, etc.), militaryapplications (e.g., locked on base, confined to quarters, etc.),conflict management (e.g., riot containment, enforcement of curfew, or“hybrid” embodiments involving military containment of biologicalwarfare with associated deployment of troops/command and control inmanaging/controlling the biological warfare agent, etc.). Thepersons/other entities do not even have to be living control/managementof deceased entities related to the reason for quarantine/restrictioncould be addressed by the invention, as deceased entities can pose asmuch (or even greater) risk to other entities when deceased. In otherwords, quarantine should not be restricted to quarantine that isinvoluntary, “imposed”, or “placed” on an individual, such as by agovernmental authority(ies), etc. Aspects of the present disclosure mayalso apply to by-necessity monitoring/control of a person or otherentity (who for their own good, if not others) that needs to be closelymonitored to prevent “involuntary” breaking of “quarantine” (such asDementia or Alzheimer's patients).

A quarantine-related exemplary embodiment may be configured to “allow” aperson under quarantine to go to the grocery store (includingproscribing when, where, with who, how, and why) based on a variety ofcontextual factors, such as the current state of the person'shousehold's pantry/refrigerator and “deployed” supplies (such as toiletpaper) all of which may be determined by using IoT sensors for example;the number of people in the household (based on any number of factors,including number of beds in the house, credit cards with that address,facial recognition “keys” enabled for the doors, etc—all determinable bysensors, or via a pre-identified profile determined at startup);patterns of usage (individually or collectively) within the household,and associated trends/predictions/projections of low inventories of agood/supply (based on daily/weekly consumption patterns detected bysensors, historical trends (e.g., rate of usage based on historicalbuying patterns of a given good, etc.); the last time someone in thehousehold went to the grocery store (based on integration withnavigation or social media apps, or integration/interface integrationwith credit card systems or grocery store databases), why they went(e.g., was it to shop, or see his cashier girlfriend—determinable basedon credit card statements, changes in household goods “inventory”relative to grocery store visits by IoT-type sensors, etc.), how long hewas in the immediate proximity of his girlfriend's smartphone while hewas at the store-based on standard personal geofence determination(possibly assisted by in store location beacons); what was bought (e.g.,using historical location/contextual/other sensor data cross-referencedto household “inventory management” systems utilizing IoT and othersensor arrays and/or cross referenced to detailed electronic grocerystore receipts); what are the household's dietary and/or health needs(e.g., diabetics, heart disease, allergies, genetic susceptibility,obtained through integration/interfaces to medical databases and/orpre-identified risk profiles as part of quarantine setup) and keydemographics (e.g., elderly, babies, also part of quarantine profilesetup); and even considering the work that person(s) in the householddoes (e.g., if they are EMTs or nurses (obtained by quarantine profilesetup or by access to employer records (with the employee's permission),or perhaps a special essential-employee electronic certificate or flagin a 3^(rd) party database)—a circumstance/demographic that can add tothe risk-of-miming-out-of-food risk by them consuming (and needing toconsume) food at a faster rate versus someone working at home (increasedrates of consumption obtained by access to calorie-consumption tablesbased on age, gender, occupation, etc.).

The use of integration or interfaces (except where otherwise noted,particularly with respect to user-interaction type interfaces such astext, email, phone calls, holograms, heads up displays, etc.) generallyare interchangeable when in the context of an exemplary embodiment. Froma technical standpoint, there are some distinctions. For example,systems integration—the kind generally used in exemplary embodimentsdisclosed herein—refers to the electronic-tying together of one or morecomputer systems, sub-systems, applications, communications networks,and/or databases through various methods and mechanisms Often thesecomputer systems et al. are owned/operated by different entities and/orhave different functions and capabilities such that integration mayinclude combining the respective functions and capabilities or evencreating new capabilities, e.g., 1+1=3, etc. An interface (not anpersonal or human user interface), such as in API (Application ProgramInterface) may also involve combining/integrating otherwiseseparate/disparate systems, but does so by providing specialcapabilities to facilitate doing so, such as providing standards andspecialized software to make it easier to integrate multiple systems,instead of “customizing” all the electronic links system-by-system,data-element-by-data-element, and so on.

Risks are also detectable in exemplary embodiments disclosed herein. Forexample, a much higher rate-of-consumption of food may be indicative of“stress eating”—a risk that, left unaddressed, could lead to thehousehold miming out of food (or certain foods) earlier rather thanlater. It could even result in “downstream” issues (e.g.,gastrointestinal (GI) tract issues, etc.). As indicated by thepossibility of “stress-eating,” mental/mentally-based risks aredetectable in exemplary embodiments. Mental/mental-based triggers suchas Anxiety or Fear (e.g., of getting sick, infecting others, or dying),Excitement (e.g., the desire to/excitement about something, or evengetting out of the house), Boredom (e.g., the “need” to get out of thehouse), and so forth, could by themselves or in conjunction with otherphysical/environmental aspects as described above could trigger—at leastin the person(s) mind—a “need” to break quarantine, which may, or maynot, meet the requirements of breaking quarantine. Certainly, being ableto detect the existence of various forms of Anxiety, Depression, orother “cabin fever” mental conditions, where the person(s) is growingincreasingly unstable, may be a “valid” quarantine-breaking condition(possibly if verified by an appropriate medical authority, who hasaccess to the sensors et. al. readings), because left unchecked couldresult in some sort of psychotic-episode. Or, perhaps, detection of suchabout-to-explode-cabin-fever conditions could cause doctors/authoritiesto prescribe (without normal seeing-in-person protocols) Anxietyetc.—reducing medications and/or other actions (such as allowing theperson out of the house once a day for local-based running underappropriate control conditions). As such, the detection and preemptionof risks are an example of the dynamic risk detection capabilitiesachievable herein, with appropriate machine learning, artificialintelligence, and/or other mechanism-based updating of pre-identifiedrisk profile and employment of new or modification of existing sensorset al., as well as creation of new risk calculations, riskpreemption/mitigation actions, resources, and interfaces toprevent/preempt the risk(s). Demographic elements, such as ethnicitycould also be incorporated, particularly if there are ethnicity-basedrisk factors associated with an illness/virus. Such special riskfactors, such as ethnicity and age, could feed into considerations ofthese factors on a macro or micro-basis. Various validation mechanismscould be employed to verify demographic risks linking to databases andalso/instead to sensor et. al. readings for measuring, verifying, and/orvalidating physical (and mental) demographic risks.

For example, local authorities could create special ethnicity and/orage-based group exemptions, allowing for example senior-only grocerystore visit date/time blocks such that only seniors/age blocks into astore for a given time period. Various validation (of their exemption“qualifications”) mechanisms could be employed to ensure only thosemeeting the qualifications would indeed be allowed to enter the store.In addition to validating that a person can leave his house beforeleaving the house, exemplary embodiments may also be configured toprovide levels or controls as to what-he-is-going-to-do level, in thiscase wanting to enter the grocery store. This further supports thenotion that quarantines will be implemented at multiple levels inexemplary embodiments. e.g., at home, at intended destination, forintended purpose (e.g., location-less context), etc.

An exemplary embodiment may include a health-related example/extensionincluding a certification or other indicator of immunity for person(s)already infected/recovered from the illness (and thus having immunity)or having “natural” immunity. Such certification, validation, or otherverification indicator may be provided in various forms, such as an“immunity card” (with a RFID chip or other sensors that could be “read”before allowing access to a space, like a store), an encrypted fileloaded on a “standard” device (e.g., phone, apple watch, etc.) thatcould be also “read” by readers and compared to other personal data viaother application integration/interfaces, e.g., a facial recognitionprogram (adjusted for mask wearing, if applicable) to scan the personholding (electronically) the “immunity card” (in whatever form) and thencomparing the embedded data (including facial profile) with a person'sactual (live) image real-time, and so forth. “Remote” mechanisms mayalso be used, such as QR codes (individualized to a person, includingwhether the person has immunity) that can be scanned at key checkpoints.Embedded chips/sensors with appropriate certification/validation couldalso be used. However, some of these “close quarters” mechanisms may beproblematic and thus avoided when conditions require social distancing.In such instances, the use of technology-based extenders and/or readersnot requiring human involvement could be utilized.

Both privacy and security may be provided to protect against tampering(e.g., trying to electronically “fake” immunity, certifications, etc.)and electronic identity theft. Such privacy/security aspects aredisclosed herein including tampering mechanisms (e.g., FIG. 4, etc.).

Other considerations addressed by the present disclosure includeenvironmental factors such as the conditions of the building thehousehold lives in (with influencing factors ranging from whether tapwater can be drunk, if there is air-conditioning in hot weather, or whatfloor the household lives on and if there are elevators)—accessed byintegration/interfaces to building records or included as part of thepre-identified profile setup; the nature and conditions of theneighborhood (e.g., crime rates, level of and proximity to publictransportation—readily identified by access to various 3^(rd) partydatabases), including likely routes (if any) between the household andthe grocery store (computed/obtained via integration/interfaces tovarious navigation apps). In “big city” scenarios where the householdhas no car and ride-hailing services (e.g., Uber service, taxis, etc.)are constrained/prohibited by the quarantine, additional focus will beput on pedestrian-oriented mapping programs (and underlying map data)identifying additional risk factors such as open dumpsters (withpossible contaminated waste), and other contextual factors such as theweather (rain or snow prohibiting/inhibiting walking—easily accessibleby integration/interfaces to weather apps or even real-time, extremelylocalized weather sensors deployed along the route or in its vicinity)or air quality (poor air quality prohibiting elderly or people withasthma from going to the store during poor quality days—obtainable viaintegration with local air quality apps/databases) and how they impactthe ability to travel safely to the store in inclement conditions on anygiven day/time (part of an invention “travel safely” risk assessmentalgorithm based on any/many/all of the above factors). Regional/localconditions could also be incorporated into the risk factors/algorithms,such as the need for certain seeds in time for planting (in homevegetable gardens), particularly given (current or predicted) contextualfactors such as weather patterns, soil conditions, dietary needs ofhousehold members, and so forth. Regional etc. variations could also(greatly) influence needs, supplies etc. by how restrictiveregional/state/local controls are on essential businesses, control ofkey goods, etc., as well as determining thepriority/weighting/conditions of who/when/why/how/where a person inquarantine is allowed an exemption, and/or even who has to be inquarantine in the first place (e.g., persons over 60 regardless ofhealth, persons under 60 with certain health conditions or comorbidity,etc.). Even the contextual nature of the physical household could beconsidered, as highlighted by someone in a rural setting with say ¼ acreof plantable land versus a high-risk urban apartment, or the “mentalcontext” of the household, detectable by monitoring various mentalmetrics of individual household members to determine their individualand collective “happiness” or stress quotient and how that quotientindividually/collectively impacts the risk of breaking quarantine.

In various contextual/demographic examples (for example, households thathave a sufficient amount of arable land), the household could—andperhaps should—be encouraged to plant a house garden or even a smallfarm. In the rural example, the household could—and perhaps should—beencouraged to plant a house garden. In such a context, what is deemedessential—or even recommended (an action that could be recommended by anexemplary embodiment(s) disclosed herein based on the household contextand other factors, such as time of year, availability of seeds andplanting supplies, etc.)—could vary (e.g., seeds would be useless in ahigh-rise apartment context, but (long-term) essential to a householdwith arable land, particularly a rural household without ready access toother food sources), and could also influence quarantine exemptions—andtype(s) of exemption(s). Recommendations (e.g., actions) by an exemplaryembodiment(s) disclosed herein for preempting a risk are a key part ofexemplary embodiment(s) of this invention, whether for example itincludes recommendations for rationing/slowing down rate of consumptionof a certain product (based on supplies and rate of consumption,prospects for replenishment, and mental “state” of the household and/orindividuals therein), or providing proactive recommendations andspecific steps for planting and nurturing a garden, or even inrecommending the household apply for a “specialty” exemption that wouldenable the household to go hunting.

In a further example/extension of the above, it would be recognized thatthe high-riser has little or no resources other than a local grocerystore or similar for food (with availability or not of readily availableonline-ordered food/supplies also possibly taken into consideration),(likely) has very limited stockpiles of goods, and would have few/noviable “specialty” exemptions that could possibly increase its stockpileoutside of a local grocery store. In a rural setting, this might not beas true, but other/different consideration for rural variations could beincorporated in exemplary embodiment(s). For example, it might take anhour or more to drive to a grocery store, placing practical limitationson access to a car, gas, etc., as well as recognition that the ruralhousehold would be logistically limited to fewer exemptions, but perhapswith a broader scope, e.g., trips to multiple stores, even visits toelderly parents (to make sure they are ok, e.g., through the window). Asmentioned earlier, other variations on exemptions could include allowingsuch rural households to go hunting to supplement their food supply.Indeed, it can be argued that in certain epidemics and quarantines, theability to self-source food could become very important to the extentpossible. As an illustration an immediate need to accurately setup—ormodify after setup—upon the imposition of a quarantine, one of the keyprofile elements relative to the risk of violating such quarantine wouldof course be the type of—and amounts of—various supplies, as well asother associated factors such as perishability, expiration dates,vulnerability (e.g., from going bad depending on contexts such asweather and storage conditions), etc. The ability to establish suchpre-identified supply “baseline” profiles, or updating the household'sprofile, via sensors, predictive estimates based on historical data, oreven manual input is reflected in the scope of this invention. Thequarantine exemptions acknowledge, account for, and enable such all theabove variations in exemplary embodiment(s), as they are heavilycontext-based and reflective of its ability to dynamically (e.g., viaautomation) detect such contexts and create/modify actions accordingly.

Exemplary embodiments may not rely on an unchanging or fixedpre-identified profile of risks, etc., but are configured forcontinually modifying/updating the profile and associated risks througha closed loop, machine-learning intensive, context-based, dynamicmethods and mechanisms. There are various ways to perform theupdates/modifications to the profile, e.g., including manually throughprompting, etc. Exemplary embodiments are configured for dynamically(and both manually but particularly via automation) updating the profileand/or include system and process integration. As disclosed herein,exemplary embodiments are directed to preemption—preemption of risks,preemptions of behaviors that might lead to violations, preemption ofother things that could result in an “official” or otherwise punitivewarning/punishment. Exemplary embodiments may be configured to help orbe on the side of the person, not the authorities—even to the extent ofnot alerting the authorities until it is absolutely necessary as a lastresort without any other choice or option

Government regulations in a quarantine situation play an outsized role,potentially controlling every aspect of life from personal mobility tocivil liberties to supply chains. Such restrictions/regulations couldchange often—even daily—and as such the appropriate 3^(rd) party datasources describing those restrictions et al. would need to be fed intothe system according to exemplary embodiment(s) via the variedarchitectural capabilities disclosed herein, e.g., communication links,directly to a client app, via 3^(rd) party servers, via distributednodes, etc., as appropriate. All of the factors/considerations could beused to establish—or update—the pre-identified profile in exemplaryembodiment(s) through a variety of means: manually, periodical(electronic) updates, and/or real-time updates to the profile viasystems integration as disclosed herein and appropriate 3^(rd) partysources.

The use of predictive analytics may be used in the exemplary embodimentsdisclosed herein. For quarantines, this is important as its notnecessarily the current supply of, say, toilet paper, it is when it islikely to run out. Thus, predictive analytics including the use ofcontext (e.g., people in the household, their conditions (e.g., GIproblems, etc.), rate of consumption, number of bathrooms, type oftoilet paper (e.g., 2 ply versus 1 ply, etc.), etc.) and dynamicmonitoring (e.g., not just frequently checking the usage, butrecognizing that changing (re: dynamic) conditions/context (e.g.,grandma is now sheltering-in-place with us, Janie's increased level ofFear is causing her gastral distress, Tom's PTSD is getting worse asconfinement continues, etc.) is very important. For example, this isimportant when predicting necessary supplies (on an absolute basis, on arelative basis compared to needs and/or certain circumstances orassumptions, and/or given/relative to a specific time period(s)), andparticularly when an exemption is needed to preempt someone beingtempted to break quarantine (and what and how that is measured, e.g.,rising Anxiety as measured by heart palpitations, monitoring ofhousehold conversations, types of web sites visited, content of messagessent, etc.).

In exemplary embodiment, the above may be utilized to identify whenthere is a “valid” need to go the grocery store and identifying it earlyenough that there is minimal (or at least reduced) risk of someone inthe household being tempted to risk breaking quarantine unlawfully inorder to obtain supplies Minimizing or at least reducing this risk couldinclude identifying “special” goods needed by individuals such that ifthey begin to get low, there is a noticeable rise in Agitation, Anxiety,or Fear levels as indicated by (personalized) values. Suchvalues—determined via various sensors as disclosed herein—includecertain amounts or percentages of raising of (in relation to normal,pre-identified levels) blood pressure, average pulse levels, and/orincrease in average decibel-based average speaking volumes, possiblymeasured in conjunction with immediate usage of a fear-of-running-outproduct, such as the use of toilet paper, for those persons who have amajor need for that product (such as people with no colon—which requirevery frequent restroom visits); or the fear/agitation is “just”identified as a major fear-of-running-out product based on other, lesslogical reasons. A “need” to stockpile—perhaps as mandated by localauthorities' or based on a variety of factors such as a baby-on-the-wayin the household, exacerbated by lack of room, money, or fear of newsources of noise—could also be included in both consideration of thehousehold's “necessary” supplies (type and quantity), as well aspredicting when an exemption would be needed. Part of the riskcalculations in exemplary embodiment(s) is to anticipate/predict therisk of unauthorized quarantine violation(s)—well before the person(s)in the household become aware of the possibility of those risk(s)themselves.

Two additional aspects address the identification of risks. One aspectis in the identification of pre-identified risks and associatedbehaviors, activities, triggers, and contexts. The second aspect is thevarious methods and mechanisms for adding to/deleting/modifying any orall of those risks and associatedbehaviors/activities/triggers/contexts. In exemplary embodimentsdisclosed herein, the invention allows for many diverse ways of addingto/deleting/modifying the profile, and in turn what data dimensions aretracked, how they are tracked (e.g., what sensors/sensor arrays/networkset al. are used), how they are configured, values/levels/ranges/tracked,how the risk/compliance algorithms are calculated/configured/calibrated,and in turn what actions/resources/integration/interfaces are employed,how the results of those actions/resources/integration/interfaces aremeasured, and how (via various techniques such as ArtificialIntelligence/Machine Learning are “fed back” into the integrated/overallsystem to further add to/delete/modify the profile, and thus ripplethrough/add to/delete/modify the risks, risk measurement systems, and soon in iterative/continuous loops.

Exemplary embodiments include highly technological closed loop orfeedback control system that doesn't rely solely on technologies, e.g.,“low-tech” mechanisms can be used to update the profile on which riskset al. are captured. For example, a person in the household could noticeon their own that a) they are getting low on a supply, and/or b) thatthey are getting increasingly concerned, fearful, depressed, stressed,or anxious about individual triggers, behaviors, activities, and/orcontexts (in a way not originally anticipated when the originalpre-identified profile was setup). Various techniques/filters could beused to incorporate (e.g., unfiltered/filtered, modified, etc.) theconcern of the member household into the profile, including direct(manual) input/updating process, via an application thatfilters/assesses the input on a variety of dimensions (includingviability, as well as assessing the risk/possibility of the personinputting data to “game” or otherwise tamper the system to try andobtain an exemption when it is not needed, etc.). In exemplaryembodiments, the system could also, for example via a Siri or Alexa-typeinterface, ask the user/household member—“Hi Sally, are you running lowon anything, or concerned,” or “what is on your mind?” “What are youconcerned about?”—a kind of mechanism-based therapy that also serves toassess risks related to the quarantine. The responses can be analyzed,crosschecked, assessed (or simply indulged), through adding theresponses to the risk of quarantine violation risk algorithm, actions,etc., including “just” automatically ordering it (what is deemed to belikely to alleviate the risk(s)) via Amazon or other source, to theextent possible. In exemplary embodiments, this kind of mechanism andinterface could be used—even if the supply request/quarantine exemptionis not “approved”—to mitigate another real risk of quarantine: mentaldistress associated with mental (triggers (or mental/physicalcombination triggers) of including but not limited to Agitation, Anger,Anxiety, Boredom, Change, Children concerns, Depression, Escape,Excitement, Fear, Frustration, Fun (or lack thereof), Guilt, Healthconcerns, (missing of) Holidays, Insomnia, Irritability, Job worries,Loneliness, Money concerns, building intolerance to Noise,Overconfidence (in a solution being found quickly), Peer Pressure (tobreak the quarantine), Power (feelings of ability to break quarantinewith impunity), Powerlessness (to do anything about the quarantine),Relationship problems, Sexual problems, various forms of Stress, upsetsassociated with Times of Days/Habit/Routine disruptions, increasedfeelings of Victimhood, and fear of Yelling or conflict. For example, aloneliness/boredom risk may be measured/detected by various methods andmechanisms as disclosed herein including context and being dynamic,e.g., why and how/what in particular in contrast to location-only. Soeven if not approved, by just the mechanism of asking the person,through Alexa, Siri, a “voice call” on her phone or even texts or emailsgenerate by bots, could be very helpful. Such a loneliness/boredom risk(measured/detected by various methods and mechanisms as disclosedherein), if beginning to elevate, could result in dynamic, context-basedactions/interfaces/resources such as automatically placing (or causingto be placed) a call to a “quarantine hot-line” todiscuss—specifically—the household's supply situation and talk aboutways to conserve them, with information about the household's situationpreceding the call to be sent to the (human) hotline personreceiving/placing the call. If not deliverable (in that sense), it couldeven be added (subject to the appropriate controls, qualifications,etc.) to a neighbor's shopping list—one who has already been granted anexemption or seems likely to be granted one before the household is. Infact, such a “shopping representative” type of situation could beemployed in contexts/situations where a person needs supplies butcannot—or is afraid to—go out to get them, and they can't be ordered anddelivered automatically.

Exemplary embodiments may also be configured for uses involving thegrouping or pooling of people under quarantine and/or include one ormore groupings or pooling of risks (e.g., everyone in the household, oreven everybody in an old building with lead pipes that need bottledwater) detection/monitoring of elements underlying those risks, and howthey manifest throughout the invention, e.g., everybody or someplurality of individuals is at risk of breaking quarantine because of acommon denominator—i.e., water; and all the way through the actions,resources, etc., e.g., someone(s) go and get water for the wholebuilding. The pooling could manifest itself everywhere, even influencingwhen and what kind of exemptions are granted (and to whom) to reflectthe common/pooled risk and the logic of addressed that pooled risk withpooled actions, e.g., delivering a truck-load of water to the buildingand then employing specialized/pooled distribution methods andmechanisms, e.g., everybody come down to the shipping dock inscheduled/staggered times; designated residents (based on relevantcriteria, such as those in the best health, the youngest adults, thephysically strongest, etc.); delivering certain numbers of water bottleson each floor, or in front of each door, depending on need, etc.

In exemplary embodiments, actions/interfaces may include peer-to-peer(P2P) communications between the person and resources. For example, inthe grocery store examples, walkie-talkies or smart watches (e.g., DickTracy-like) could be used to make it much easier to communicate from a(local) distance, without using a phone. Plus, touchscreens ofsmartphones are not usable while wearing most gloves. Walkie-talkies(that use a manual push button) could be “standard issue” (sanitized ofcourse) as a person walks into a store, to communicate with storepersonnel, once the person is verified to be wearing the appropriateprotective gloves, or, better yet, voice-activated communicators thatrequire minimal physical interaction. Such P2P communications could alsobe employed with home deliveries, and/or assistance with deliveries,and/or “pooling” of resources, among other uses.

In exemplary embodiments, a core capability of the invention is toidentify the “triggers”/certain behaviors/activities/states (mental,physical, or both) of a person by which, if left unaddressed, could leadto undesirable behavior—in this case, violation of quarantine. This riskof quarantine violation, however it is defined for the circumstances,could be a major input into an “application”/submission to theappropriate “authority(ies)” for a (temporary) quarantine exemptioncertificate. Here again would be where various privacy/securityprotocols/mechanisms would be employed to prevent a household from“gaming” the system to generate an “inflated” risk of quarantineviolation by manipulating various risk factors and so forth. In turn,the exemption authority could incorporate the household's individualrisk assessment along with other considerations, including, but notlimited to, the number of other requests/submissions; their geographicaldistribution to each other and to local grocery stores or other neededsupply source(s); the timing of the risk (e.g., who is most imminent atrisk of breaking quarantine); the goods listed in the submissions ofbeing most at need; the “priority” of those goods (e.g., toilet paperand protein products would be much higher than ice cream for example);the supply of those goods at those grocery stores; the expected traffic(and routes) if exemptions were issued (e.g., tailoring whichsubmissions would be approved, and when, in order to managepedestrian/road/store traffic) Similar elements would be involved in theobtaining of services, such as needing a plumber for plumbing problems,with additional considerations/precautions required given the need for aplumber (in this example) to be specially screened relative toquarantine requirements and the individual household needs andsituation, such as requiring full hazmat suits if there is an elderlyperson in residence. This submission evaluation/approval process mightbe a multi-step process; first, a tentative approval might be made, andthen second, once additional analysis (e.g., selection of householdmember, preparations have been made) then a second—much moretime/store/route-specific exemption would be issued, along withassociated systemic verification mechanisms (e.g., disabling any alarmsthat might be set on the household's door, updates to police databasesto make sure that the specific exemption is set up in theirenforcement/tracking databases, etc.).

Supplies may also be delivered to the house through delivery services,e.g., Amazon, etc. In such a case, the details associated with ahousehold member leaving quarantine to shop would not be applicable. Butthat doesn't eliminate the risk of exposure to the illness even ifdelivery is allowed. Further, the nature of the illness-causing entitywould be very important in terms of precautions that would be requiredto take place before any shopping or even the person being allowed toleave the household. For example, air-transmitted viruses may requireextreme attention to face/nose coverings and social distancing, whereassurface-spreading diseases would require gloves, and extreme attentionto minimizing any sort of contact to any surface, human, even pets,elevator buttons, etc. The aspects employed in this invention can bedependent upon the full range of details involved: the nature of thedisease/situation; the requirements of the quarantine and quarantineexemption, both overall and locally; the demographics and otherindividual characteristics of the household; the context/characteristicsof the environment and household; the needs of the household (and ofcourse individuals within the household); the availability and sourcingof the goods and services needed to fulfill those needs; the logisticsinvolved in fulfilling those needs; various timing/time frames involved(e.g., how long the quarantine/current quarantine level is expected togo on; the nature of and/or priority of the goods and services involved,such as perishability of goods, priority of service needed, etc.); themacro availability and logistical complexity of the relevant goods andservices (e.g., is rationing needed), the ability to deliver—in whateverform—the goods or services to the household, and so on.

As noted before, a key goal of this invention is not onlypre-empt/prevent quarantine violations, but to preempt/prevent the“upstream” behaviors/triggers/associated risks that may, if leftunchecked/unmodified, lead to a possible violation. For instance,example embodiment above have to do with possible violations caused bymiming out of toilet paper, or plumbing problems (perhaps caused byusing too much toilet paper). An objective of exemplary embodiments ofthis invention would not be focused on the miming out of toilet paperper se, or detection of a clogged toilet, but to identify and pre-emptthe behaviors that might in turn be causing a high consumption of toiletpaper. It may be that a certain person(s) in the household has agastric-related medical condition, with confinement exacerbating thatcondition because of his/her Anxiety, Fear and/or Stress trigger'simpact on gastric irritation (and, in turn, increasing the amount ofbathroom trips and amount of toilet paper consumed). Exemplaryembodiments of this invention would be able to detect these triggers(either their existence or levels that are deemed as elevated/high) asearly as possible in quarantine (perhaps benchmarked with pre-quarantinetoilet paper consumption data), and proscribe actions to pre-empt hightoilet paper usage, proactively, instead of reactively trying to orderhigher volumes of toilet paper once the trigger/health condition isfully “active.” Towards that end, exemplary embodiments of the inventionmay proscribe the need for virtual mental therapy to address the Anxietyetc. trigger(s), and/or perhaps recognizing that therapy may not work oronly be partially successful, proscribe (and even automaticallyordering) a bidet that will cut down on toilet paper usage.

The above example is indicative that triggers may be either or bothmental and/or physical. As disclosed herein, triggers may encapsulatevarious forms of mental thoughts, mental and/or physical behaviors,mental and/or physical states, and/or mental and/or physical activities.For example, a mental trigger may be Fear, a mental/physical combinationtrigger may be Anxiety and Health, etc. In particular for quarantine,mental triggers Anxiety, Depression, Fear, Frustration, Insomnia,Loneliness, and Powerlessness may be major triggers at issue withphysical elements being secondary. Health may also be a primarily mentaltrigger, such as when a person is not sick, it may be the fear ofgetting sick (e.g., hypochondria) that is the issue, or if the person isactually sick or shows symptoms of being sick, it may be the fear ofdying or infecting others that is the issue. Accordingly,mental/mentally-based triggers may also be in conjunction with physicaltriggers.

There are many more detailed exemplary embodiments that are enabled bythis invention. For example, through its “support resources”integration, the invention would also be able to access the profiles ofany potential delivery or service persons, including their immunity (ornot), familiarity with and/or access to the household (e.g., could theybe allowed to bring in groceries without the need for a household memberto open the door for them or otherwise get anywhere close to a householdmember, and so forth).

A variation on the above embodiment enabled by this invention is the useof “flash” delivery points to allow delivery person(s) and recipients tomeet in an area/controlled environment to deliver and pickup goodssomewhere else other than, for example, a household or store. Suchmeeting places in addition to being “fixed” (e.g., post office boxes,etc.) could be dynamic in nature (e.g., short-lived, even setup purelyfor that particular date, time, and context, specific package type,persons needing to be involved, location otherwise used for otherpurposes, etc.). Such meeting places may be similar to the “flashmeetings” as disclosed herein, e.g., FIG. 10 illustrating example waysin which Real-Time Location System (RTLS) technologies can be used toenable ad hoc, spontaneous, unscheduled, or flash addict meetingsbetween people with similar addiction issues. Such meeting places may bebased on identifying meeting places and appropriate related contexts(e.g., sterilized, secure/exception-qualified credentials, meetingplaces appropriate for/accompanied by other resources for safe handling,middlemen-type services, etc.) to allow for the safe “transfer” of thepackage from the delivery person to the recipient (in real-time,scheduled, or asynchronous delivery) dynamically (e.g., thelocation/context being short-lived, and potentially established purelyfor this package only or perhaps similar packages or contexts (e.g.,people with similar issues and contexts also using the “flash” deliverypoint. Further, exemplary embodiments of the invention enable“context-based” delivery, e.g., given the constraints of a household(e.g., an elderly woman who can't pick up anything more than 5 pounds),the size/weight of the package (e.g., a pack of 48 water bottles), andthe capabilities of (e.g., weight-lifting capabilities being up to 50pounds—less than the pack of water bottles) and “health-certification”of the delivery driver (e.g., has been certified immune), that whendelivering an over-weight-limit package to that weight-constrained,particularly-virus-sensitive-and-thus-requiring-extended (12 feetinstead of 6 feet social distancing)-social-distancing-household, thatthe delivery/delivery driver would be allowed special delivery“exemption and context-based instructions (action) to allow in-homedelivery of the package (even allowing electronic access to the homewhile the household member practices social distancing) and even“setup,” such as bringing into the house, wiping down the package, and“putting away” the package (such as a shipment of 48 water bottles) inthe house.

A specific sub-embodiment of the water bottle scenario is furtherillustrative of the novel and inventive aspects of the invention, andhow it contrasts with existing, location-only prior art. For example,consider an elderly gentleman who lives alone in a 3^(rd) floorapartment in a 100-year-old building with no elevators or airconditioning, nor does he have a car. The nature of the disease causinghis quarantine dictates that he has to drink bottled water, as hisbuilding has lead pipes and the nature of the virus and his healthconditions means drinking tap water is hazardous. According to exemplaryembodiments, the invention (e.g., systems and methods, etc.) isconfigurable to know or have knowledge of the last time he went grocerystore shopping, which store, and what he bought, particularly how manywater bottles he bought. Internet of Things sensors (IoT-type sensors)may track his consumption, and predictive algorithms based on hisconsumption patterns, adjusted for weather predictions (e.g., how hot itwill be and how his consumption will increase) may predict when he islikely to run out of water. Since a key goal is to preempt/prevent therisk of the man violating quarantine to get water (or, alternatively,risk his health by drinking tap water), the next part is to determinethe best set actions, resources and integration/interfaces for him toobtain that water, before the risk of miming out of water/having todrink tap water becomes too high, or he is tempted to violate quarantineto the risk (of infection) to himself or others (in the event he is anasymptomatic carrier). In this sub-embodiment, support resources(including the delivery driver example) would become criticallyimportant. As illustrative throughout the disclosed exemplaryembodiments, the dynamic determination and deployment of actions andsupport resources, via context-based interfaces—including creation ofnew actions et al. to address unforeseen contexts, utilizing AI, machinelearning, and/or drawing from similar contexts beyond the individualhistory of the person(s) involved—are key elements in exemplaryembodiments. As disclosed herein, dynamic, context, machine learning,multiple support resources, and closed loop aspects are also importantin exemplary embodiments.

Picking up with a household-needing-to-go-to-the-store embodiment, thereare numerous context, location, and dynamic sensor/network basedelements that factor into identifying what store to go to (presumingdelivery is not available in this scenario), when (day/time) to go, whoelse will be involved (in helping him shop/bring stuff home), how hewill get to the store (walking and subway, and what route is best totake), what to buy (given his water requirements, what he's able to gethome, and up to his apartment).

The above-described exemplary embodiments and its variations are enabledby aspects of this invention, though some exemplary embodiments arefocused on preempting the core risk of violating a quarantine and/or avalid quarantine exemption exemption, through focus on various“upstream” behaviors/activities/states/triggers/risks.

A particular concern, and highly important in calculating the risk ofquarantine violation—not just risk of a violation itself, but the riskto public health—is in the case of a household member having an active(e.g., contagious) case of the sickness. To the extent that tests areavailable to determine contagiousness or not, they would be employed,and taking a test and obtaining the results would be a prerequisite toobtain a restriction. Variations of risk could be considered based onthe type of test(s) employed, e.g., antibody tests, micro blood testingstations, small microneedles sampling blood and rapidly scanning forinfection. Other tests might include a thermal camera or infrared (IR)contactless thermometer, just doing temporary scans, any or all of whichcould impact the type and/or duration of any exemption.

The test results could result in an immediate update of an “immunitycard” (physical or virtual) or equivalent, and/or be sent to the variousendpoints (e.g., grocery stores) or other in-route/possible-route nodes,as needed. In the early days of a pandemic, tests may not be available.In such circumstances, it is to the benefit of all—not just “forcing” asick person to go out for (possibly desperately) needed supplies, butfor the sake of anyone that they would interact with in the process—todo everything to prevent a situation. In such a situation, not onlycould the person's priority be dramatically escalated (particularly ifthey are a single-person household, but also even multi-personhouseholds where non-sick persons may be asymptomatic and thus for allpractical purposes be as dangerous as the obviously sick symptomaticperson), and, instead of applying a household exemption, insteademploying (probably scarce) support resources to do the shopping forthem and to deliver them to the household. In this scenario, the supportresource(s) would need to have an equivalent exemption made for them,but with varying conditions such that they are not as subject to asstrict routing or time windows, do not have their own personal futureexemptions somehow impacted, and indeed to the contrary those“do-gooders” (even if paid), are providing future exemption “credits” orother rewards, including but not limited to those disclosed herein,e.g., FIG. 9 describing an example addict rewards/demerits system basedon an addict's behaviors and actions, which may include rewarding (orpunishing) an addict based on behavior via tracking and data analyticsand various reward mechanisms, etc.

Embodiments/sub-embodiments disclosed herein are/can be applicable toParolee/Persons Under Restriction as well as being useful in relatedforensics applications. For example, the information/data generated maybe used to actually forensically prove related crimes, for example,proving that an “alibi” that a person dies from a virus was insteadactually murdered, and so forth. In this exemplary forensic application,the risk avoided or preempted by the system/method in this example wouldbe in allowing someone to literally get away with murder. All sorts ofdata that is collected as part of the quarantine risk of violationpreemption as disclosed herein could be also used in reverse-engineeringcrimes, including murder, etc. For example, if there is evidence thatthe wife disappeared 10 days ago, but the husband maintains shedisappeared two days ago, various data could prove/disprove that, suchas how the rate-of-consumption of toilet paper changed 10 days ago, not2 days ago.

It is worth reiterating that a core purpose of exemplary embodimentsdisclosed herein is to preempt a behavioral/activity/trigger-basedviolation from ever occurring (e.g., breaking quarantine, etc.), bymonitoring/tracking/assessing and preempting risks ofbehaviors/activities/states that, left unchecked, might lead to actualviolations, long before there is imminent risk of such actualviolations. Another core purpose is to preempt/prevent the person goingto the store from being tempted to (or accidentally) violaterestrictions associated with how he/she goes to the store. In all theseexamples, this may be implemented (more) for the purpose of protectingindividuals in the traumatic event of a quarantine, rather thanimplementation for use a tool for government to monitor individuals andimpose penalties on violators.

If it is determined that a trip to the grocery store is needed toprevent serious risk of violating quarantine, exemplary embodiment(s)would enable several other activities and monitor associatedbehavior(s)/activity(ies)/state(s)/trigger(s) that, if they occur (or,show signs of starting to occur, or being a risk of occurring), couldlead to violations (e.g., revocation of current quarantine exemption,lowering the possibility of future exemptions, public shaming, arrest,etc.). As noted before, the invention could for exampleintegrate/integration/interface with quarantine exception systems/app(s)(perhaps run by the local government) to request an electronic exemptioncertificate, providing some of the key information described above andbelow. Once—in general—an exemption is approved (pending details, assupported by the 2-step local government process discussed above), thedetails must be determined, such as person(s) who will be the designatedshopper, which store(s) will be allowed to be shopped at, what route(s)is/are allowed, what time(s) has been allotted for that specificshopping visit(s), what variations in the above are acceptable (e.g., ifit is raining, more time might be allowed). Or if a critical item isout-of-stock by the time the person gets to the store, an extraallotment of time (along with new computation of routes and associatedrestrictions) may be allowed to enable him to get to a “secondary”store, etc. These details could be stored on a device/mechanism on/in/inthe vicinity of the person, or they could be stored elsewhere andretrieved in conjunction with various personal identifierson/in/associated with the person, and/or stored in various distributednetwork nodes (potentially with distribution of details according towhat a node “needs to know, and be known” relative to when the person isin the “coverage area” (logically or physically) of the node).

All these details may be important, as for a quarantine exception tofunction, it has to be relatively limited (or at least well defined),including a start time window, the start (e.g., home) and particularlythe destination (the specific grocery store allowed), the ending timewindow, and what variances/flexibility (if any) is allowed in routetravelled, destination(s), method of travel, etc., as well asenvironmental and other factors associated with all the above (e.g.,weather, crime, garbage dumpsters, etc.). These details would depend ona variety of considerations, which considerations are anticipated by andaddressed by the exemplary embodiments. Various variations in geofencetechniques may be included as well as the context-related capabilitiesdisclosed herein.

If a risk of someone possibly being tempted to break quarantine isidentified (in the form of an absolute score, a relative score incomparison to other households of similar composition/characteristics, arelative score in comparison to pre-quarantine metrics/“benchmarks,and/or risk categories or even specific qualitative/quantitativeexamples of specific risk manifestations), and/or a recognition of thehousehold's need (by local authorities, for example) has beenacknowledged by the authority(ies), a next step (in a multi-personhousehold) would be to “select” the household member (or this selectionmay be made by the exemplary embodiment(s) via assessment/computation,and in turn enforcing its selection) to go on the grocery store run(assuming only 1 person per household was allowed out). The selection—ifnot left to the household—could in turn depend on any number of riskfactors, including but not limited to health and mobility factors(determined by pre-identified profiles and/or various sensors and/oraccess to various databases, as described before), such as age (e.g.,not being too old or too young, for reasons such as being able tophysically perform the shopping), whether he/she has positive ornegative health factors such as virus antibodies (e.g., was alreadysick, as discussed earlier), pre-existing health conditions (puttinghim/her in extra danger if the virus was caught while they were outshopping), and/or genetic susceptibility (to catching the illness and/orthe illness will be extra detrimental given their genetic conditions).Any/all of the above could also be evaluated against local contextualfactors such as weather, crime, etc.

Further, mobility factors could be considered, e.g., does the personhave access to a car/safe transportation, or whether the person ifhe/she has to walk has demonstrated a past ability to walk the requireddistance and shop, have the ability to carry/push a certain weight andbulk of groceries, and return in the allotted time; and/or other riskfactors such as the possibility or propensity to get “distracted” on ornear the route needed to go to the selected grocery store (such as, forexample, him having friends/a girlfriend that live along/near theroute). Note that “safe transportation” by itself would require a riskassessment calibrated for the day/time that the person will betraveling. Certainly not all public transportation options will be equalin terms of safety relative to the person's specific requirements, andeven private transportation options (e.g., bikes) may have practicalobstacles that prevent their usage, in the specific context for whichthe person would need it (e.g., it is impractical to carry heavy waterbottles on a bike). All these factors could be pre-identified as part ofthe profile setup process of the invention, and/or various sensors,and/or could be computed by the exemplary embodiment(s), such ascombining various personal characteristics into a “ability to shop whilewalking X distance with Y pounds” score). In exemplary embodiments, theinvention may also incorporate the use of “Support Resources,” such asthe availability of (immune) nearby strong young persons to help elderlyin carrying groceries from the store back to their apartment on the3^(rd) floor. Requesting/needing the support of additional resources(inside the household but especially outside the household) may be partof the exemption request process and may include requiring access tomany of that support person(s) personal and/or household data as part ofthe exemption request risk assessment and evaluation/approval processes.

In the shopper selection (or preparation) phase, exemplary embodiment(s)could also incorporate current local and/or demographic-orientedofficial guidance regarding movement/shopping and associated conditions(e.g., use of masks and gloves, and considering the household inventoryof such). If masks and gloves are required, for example, then thatrequirement could be included in the exemption “certificate” (orequivalent), a potential source of a violation. Towards that end, if thehousehold/shopper does not have a mask and/or gloves (e.g., medicallyapproved, not just winter gloves, etc.), then an expedited acquisitionprocess could be initiated, interfacing with Amazon or other onlinestore, or perhaps local government stock, to get them expedited shippedto the household. This latter step would ideally not happen. Instead,one of the immediate tasks upon implementation would be to ensure thateach household had all the medically necessary “pre-requisites”necessary for not only surviving the quarantine, but also have what isnecessary when it is necessary (and allowed) to venture outside theirindividual quarantine area.

A precursor, concurrent, or next step at this point is determination ofa complete—and verifiable—shopping list (virtual and/or physical). Thislist would be predicated on (but not necessarily limited to) thehigh-risk item(s) (that is/are causing the risk of quarantineviolation). This list could be open ended, e.g., whatever householdwanted (as long as it included the high-risk items); or it could bepartially restricted (say, to total estimated weight); or it could berestricted to specific, individually approved items and quantities bythe authorities. And/or it could be adjusted/restricted depending onsupplies/anticipated supplies in the store(s) that the household memberhas access to, based on geography and other factors, discussed next.

Turning to the “destination” of the quarantine, exemplary embodiment(s)could also incorporate current availability of certain foods at thenearest grocery store versus what the household is getting low on and/orneeds/desires, and even selecting the grocery store(s) based on current(or projected) traffic in the store. For example, if there is less foottraffic/people in store A versus store B perhaps measured on a squarefoot basis, or number of active cashier lanes at that time or projectedfor when the person will be there, Store A would be selected even ifStore B is closer. The exemplary embodiment(s) could even incorporateinformation like pedestrian traffic (current or projected), if theperson would need to walk to a store and designing a route thatminimizes interaction/maximizes the possibility of 6 feet or more socialdistancing, at various possible specific dates/times that are underconsideration to be assigned/allocated to the person. Suchassignment/allocation of permissible go-to-the-grocery-store times couldeven extend to integration with, and coordination with, schedulingprograms, e.g., a person is allotted a coveted spot at 5 p.m. Fridaybecause the person is a high priority go-to-the-store person based onthe above, and the person is not scheduled to be at work at that time. Apotential key part in the grocery store selection would be current andprojected inventory of all items in order to make sure that an exemptionand selection of a specific store does not go for naught by the personshowing up to a store that is out-of-stock. This risk could be preemptedby ties to the store's inventory management system, e.g., timing thestore visit with expected delivery times (and stocking) of the high-riskitems; use of an electronic ration-book control system; and/or apre-allocation of stock by the store to a specific person (or exemptioncertificate), essentially setting aside the inventory for that person(or certificate) only. This would include re-introducing the stock intoinventory, or allocating it to another person, if the item(s) are notpicked up by the exemption certificate expires.

The exemplary embodiment(s)—at any point in the process—could alsoincorporate the household financial situation (based on pre-identifiedprofile in the setup process, and/or access to credit score, etc.),e.g., income levels, whether the household is on food stamps that areawarded at the beginning of the month (thus being slotted at thebeginning of the month is better than the end of the month), or eventake into considerations when scarce supplies (e.g., toilet paper) thatthe household needs (or has been allocated a monthly “ration”) will beavailable in Store A, and if that is one of the household's pressingneeds given the rate of consumption in the household (projectedconsumption based on household demographics, or even actual consumptionas measured by IoT-type sensors). The financial situation of the storesin the area of the household could also be incorporated into thealgorithms involved in choosing the store(s) that could best serve theneeds of the household at that time.

In addition, an electronic “certificate”—with an embedded duration andwhere—can-be-used embedded, could be issued to the person authorized togo to the store, that could be monitored by various sensors in thestreet, and actions and/or alerts of various content,integration/interfaces, and intent, sent to various enforcement entitiesif the certificate is missing or not valid at that time, place,location, and purpose. A core purpose of the invention is to preempt anyactual violation. As such, the invention's primary—and firstpriority—purpose and associated actions, etc. is toinform/influence/mitigate a person's behavior before an actual violationoccurs. Thus, warnings/alerts etc. would be sent to the person (supposedto be) shopping or on the way to/from shopping, to get them to address“inappropriate” behavior well before an actual violation occurs and inturn triggering all manner of unpleasant actions by law enforcement etc.being visited on the person.

For example, any deviation from the authorized routes by the person fromtheir home to the selected grocery store could be analyzed andpredictive analytics employed to determine if the person is accidentally(or within acceptable parameters) deviating from its authorizeddestination of the grocery store, or warnings/alerts sent to the personif their departure time or rate of progress is too slow relative toexpected shopping time (estimated based on shopping list, traffic, andaverage through-put times of the store as historically indicated forthat date/time or projected/predicted). If for example, the person usingthe “authorization” seems to instead go (or start to) to a nearbyfriend's place, in violation of official gathering guidelines, could bedetected. In keeping with the invention's emphasis on extra-early earlywarning, and the preemption/prevention of an actual violation,indicators that indicate such misuse of the authorization could bemonitored via a variety of sensors/networks et al., ranging from nottaking a wallet (e.g., not taking any money to buy something), text(s)to a friend that says (be there soon) to dressing up “inappropriately”(e.g., getting all decked out in a way unlikely to a grocery storevisit) to not taking a mask and gloves (prerequisites for being allowedto entering the grocery store), and so forth. Even checking that theperson's primary device(s) has a sufficient amount of power for the tripcould be part of the risk preemption mechanisms (e.g., if the phone diesin route, this would create all sorts of problems—mostly unmitigable).Again, the emphasis of the invention would be on actions to pre-empt aviolation, through extra-early detection (particularly usingbehavior(s), activity(ies), state(s), trigger(s), and/or context(s)) andfocus on actions, alerts, etc. to the person, versus prior art thatfocuses on location-centric, imminent or already-in-progress violationsthat are focused on involving law enforcement or other authorities.

Once a person has been designated to go to the store, and the conditionsunder which he can go have been defined (e.g., what store(s) and where,under what date/time window(s), with what support resource(s), whatroute(s), by what mechanisms (e.g., car, walking, Uber, taxi, bus,etc.), and what can be purchased, to address the overall underlying riskof breaking quarantine and what individual risks making up that broaderrisk (e.g., the elements of where, when, who, how, what, and why), thenthe “processes” of actually going to the store, doing the shopping, andreturning need to take place. This includes the management of a seriesof related risks, including but not limited to: violating any of therelated who/what/when/where/why/how aspects such as leaving too late ortoo early; going without appropriate personal protective equipment(PPE); going with unapproved resources; deviating from the approvedroute(s) (or taking routes that could jeopardize being able to completethe shopping in the window allowed); going to the wrong stores (orunapproved stores); interacting with person(s) not deemed essential tothe shopping (for example, stopping at a friend's house along the way);not purchasing the supplies that underlaid the reason(s) for theshopping in the first place; purchasing goods that might jeopardizebeing able to complete the shopping in the time allowed (for example,buying a 50 gallon container of water that could not be carried withoutassistance up the 3 flights of stairs up to the household apartment),for example. All the above can be tracked/monitored via some aspects ofthe exemplary embodiment(s). To enable at least some of this in-route,while shopping, and/or getting goods home sub-embodiments, it will benecessary to have integration/interface/integrate with various deployedsensors, networks, systems, et al. along the way and/or in the store.Accordingly, this exemplary embodiment may rely upon context includingWhere, When, Who, but also What, How, and perhaps most important of allWhy. This use of context is considerably different than prior art thatrelies upon location only (Where When Who without any context and withWhy).

There are numerous—and perhaps an unlimited or countless number of—waysfor “validating” that a person outside their home is doing so “legally”or otherwise in an approved fashion. For example, just leaving thehousehold could be validated with a geofence-based validation that theperson leaving (or about to leave or having just left) a geofence(defined by any number of methods, such as the property footprint for asingle-family home, the actual footprint of the home, going out of therange of a beacon placed within an apartment or on the door(s), etc.).Upon detection of a (possible) violation, various devices associatedwith the household could be checked for an exemption certificate orequivalent, and local, EDGE-type IoT computing could prevent any furtherescalation of possible violation checking, at least with respect toimmediacy of household leaving risks. Or, such possible violationbehaviors could be communicated electronically to central server and/ordecentralized nodes in the vicinity of the household to confirm a validexemption. Such geofence-type checking is addressed in the prior art, orcould be considered obvious, even if the mechanisms (e.g.,EDGE-computing and validation via decentralized nodes) were not.

But a device leaving the geofenced area or crossing a geofence is notthe end of the exemption checking. See, for example, the tamperingmechanisms disclosed herein (e.g., FIG. 4, etc.). The tamperingmechanism(s) may be used to make sure that the person leaving thehousehold (with the device with the approved exemption) is the one whoshould be leaving. Although verification mechanisms may be known, thequarantine exemption—with a stated destination, allocated time, etc.—canalso be context dependent. For example, various sensors (in thehousehold, in/on the person, etc.) could detect the existence of—andproper usage—of masks and gloves, if for example those are required orrecommended by various authorities (the existence of determined viasystems integration with appropriate 3^(rd) party systems/data sources).The exemplary embodiments may focus on determining this as soon aspossible prior to leaving the vicinity of the household (or even beforeleaving the household), generating certain alerts/actions if the masksor gloves are deemed to be missing (e.g., go to pantry #3, they are onthe bottom shelf) or not being used properly (e.g., the mask is aroundthe neck, not over the mouth and nose, in proper orientation andcoverage). Even non-required elements could be detected, such asbringing reusable bags in an environment is discouraging (but notprohibiting) use of such bags (due to their retention of germs), or,alternatively, since part of the shopping list may include heavy items(that would cause plastic bags to tear), ensuring (via thealerts/actions and subsequent results measurement) that the reusablebags being brought have been appropriately disinfected before leavingthe household.

As an example of the dynamic, context-based actions/alerts of theexemplary embodiment(s)—and a distinction between geofence (e.g.,location or location/context)-only based alerts—can be seen in theselection of interfaces in the above leaving-the-house example. Theinterface could vary for example both with respect to where the personis relative to the geofence boundary as well as the specifics of whatthe risk is. For example, if the person is still in the house, but nothaving opened the door, without having yet picked up masks and gloves,an audible alarm from in-house speakers could be generated with amessage of “don't forget your mask and gloves!” Once he crosses theboundary (e.g., opens the door and steps out), then a buzzing or beepingon his phone could be activated (without an audible voice, to preventpanicked listening by neighbors). If he still hasn't turned around andgone back to his house, various automated calls and/or texts could besent to all his devices, escalating if he continues to ignore them, suchas sending messages to any support personnel (accompanying them, orbeing planned on being met) to try and contact the person/get theirattention. If in a different context (e.g., he left carrying reusablebags), then the nature of the actions/alerts, and their content, mightbe dramatically different, reflecting the difference in severity betweenan “essential” risk of violation (e.g., leaving quarantine withoutproper PPE versus an “optional” risk of having discouraged (but notprohibited) reusable bags). Further, to the extent the various actionsand/or interfaces (and or additional resources employed, if any) werenot effective in changing the person's behavior (and thereby increasingthe risk of an actual violation), then these (non) results would be fedback into the system/method embodiment(s) of the invention (viamechanisms including AI and machine learning) to modify (or even createnew) actions/interfaces/resources to be employed instead of the thoseused in the current actions/interfaces/resources, the next time asimilar risk, behavior, and/or context was detected, so that preemptionof the behavior(s) that might lead to an actual violation could be donebetter/earlier/in a more effective manner.

The above leaving-the-house example helps to demonstrate (novel andinventive) context-based extensions of “traditional” geofence-basedrestrictions, not only in what is tracked/measured, but how variouscontext-based risks are identified (e.g., masks, reusable bags, etc.),and dynamic, context-based generation of actions are employed, evenwithin a context-type (e.g., instead of the context-based possible riskof violation being not having a mask, it is one about how the person notwearing the mask properly, with variations of actions and interfaceswithin even that same context-type, even before he reaches the door)—allexamples of dynamically detecting, and preempting, various risks, viavarious behavior/activity/trigger/risk/context-based methods andmechanisms, before they can become actual violations.

As the household member (and potentially, support resource(s)) travelsto the grocery store, his progress will be tracked. There are a varietyof ways to do this, e.g., by traditional GPS, or via Wi-Fi Real-TimeLocation Systems (RTLS), or beacon-based tracking, or other suchsystems. While such this tracking may be known per se, what is done withthe tracking information in embodiments disclosed here innon-conventional and less traditional. Given this invention's focus onpreempting violations, the invention's primary—and initial focus, atleast—is in detecting behaviors that might lead to violations, andinitiating context-based action, using context-based interfaces andresources, to “course-correct” the person before an actual violation canoccur and/or be reported. Exemplary embodiment(s) would go to greatlengths to prevent a violation from being reported, including (undercertain circumstances/contexts), not reporting actual violations toauthorities if there is any chance of the person (quickly) correctingtheir behavior. For example, if a person is deviating from their“approved” course—even to the extent of exceeding a “tolerance,” bufferzone, or other approved range of variance (measured in distance, time,or other metrics), the exemplary embodiment(s) would implement anincreasingly strident series of actions and interfaces and/or resourcesto “encourage” the person to correct their behavior, potentially endingwith a kind of “last warning” message (via ALL possible interfaces),after which actions/alerts/messages to the appropriate authorities wouldbe implemented. In this set of tiered or progressive actions, et al. thecontext(s) of the person will be critical in determining thetiering/progression, as well as to what degree of “leeway” orflexibility in the tiered/progressive actions will be implemented.

For example, if the context is that the person has a broken bike (atransportation alternative somewhere between a car and walking, and acontext detectable by sensors on the person's devices, on the bike, oreven self-reported) and the person appears to be seeking repairassistance somewhere off the approved route, then the invention wouldnot only not report the deviation to authorities, it would recognize thecircumstances and try to help by contacting other (non-infectious)resources in the vicinity with the necessary skills and/or directing theperson to the nearest bike shop that is allowed (in emergencycircumstances) to repair bikes. Even if such a specific context cannotbe detected, then the exemplary embodiment(s) could make differingtiered/progressive decisions and associated actions et al. based on notonly where he is, but how he is moving (e.g., miming, getting in a car,etc.), and using that information along with historical data on hismovements and contexts as to where he is likely to be going. Thatpredicted “final” destination would then likely have dramatic impact onthe actions et al. employed by the exemplary embodiment(s). If it lookslike he is going to a hidden, prohibited party for example, the actionset al. could be quite different than if he appears to be wanting toquickly visit his mother.

Once at the store, a new round of sensor usage is employed, sometraditional and some via this invention. To start, just entering thestore will require validation of the person's “credentials,” e.g.,having a valid exemption, and validation that the person trying to enteris indeed the one on the exemption (checked locally, centrally, viadistributed “smart” nodes, etc.). This may include various techniques,including facial recognition (adjusted for mask-wearing). Context couldcome into play, however, for example, if the store has differententrances for different needs, than those contextual needs (e.g., theshopping list of the person/household, or at least their critical needs)could further modify the validation “protocol” or equivalent of thestore. It could also vary for example by the age and (non-virus) healthconditions of the person (e.g., a separate section for senior citizenswho are otherwise healthy). A further variation could be if the personis accompanied by support resource(s) or needs support resources withinthe store.

As the person traverses the store (practicing enforced social distancingusing for example, personal geofences, or beacons that permit only 1person at a time to be within range), the person's shopping list iscompared to the goods on the shelves (using various tags/beacons peritem). For example, persons with the store may be require wearingwristbands that vibrate when someone in the store comes within apredetermined distance (e.g., six feet, etc.) of another person in thestore, whereby the wristbands would thus provide personal geofences foreach person (e.g., shopper, cashier, stocker, other store employee,etc.) in the store. If the person passes by a needed (e.g.,required/essential item that was a factor in their getting an exemptionin the first place), then various alerts could be enacted. These couldbe delivered via a “standard” interface (or even specialized appautomatically loaded on his device of choice when he entered the store),with alerts escalating if he continues to pass by the item withoutpicking it up (in other words, enforced shopping of a sort). The natureof these actions could also be tiered, generally localized to theperson, support person, and/or store management, e.g., not such as todisturb the other shoppers. Exemplary embodiment(s) could even enable“assisted” shopping in a sense that if a person “misses” as certainitem, store employees will “pick” the item(s) (in the allowedquantities) and have it ready for the shopper when they get to theregister. Indeed, at checkout (preferably via contactless mechanisms),the shopping list of the person could be automatically checked againstthe “required” items. Any omission would result in the total (includingthe item) to include the missing item(s), and then requiring the personto wait (probably outside) while the items are picked and “delivered” tothe person (preferably without human interaction). In exemplaryembodiments, other considerations may include a comparison of (A) howmuch time the person has left, ability to carry items, distanceinvolved, how travelling (e.g., subway, car, bike, etc.) with (B)whether a (missing) item should be (involuntarily) enforced via perhapsa dynamic (context) calculation of total “allowed” purchases” (e.g., byweight if the person has to carry the purchases and/or othertransportation alternatives, etc.). A predictive algorithm may be usedto determine if the person cannot get home before the exemption expires,and if so, then a “forced” replacement of non-essential items may bemade with essential items.

Leaving the store to return home could be tracked just like going to thestore. But here again, the different context (e.g., going home, with aload of groceries versus going to the grocery store with none) couldresult in significantly different actions/interfaces/resources beingemployed. For example, any deviations from the route could be cause fordifferent actions et al. to be taken than a similar deviation in thegoing-to-the-store context. A bike breakdown, for example, would makegoing to a bike store with a bunch of groceries impractical, if notimpossible. As such, (approved) bike resources would need to come to theperson. Such a situation would very likely result in an exemptiontime-expiration if left unaltered. Thus, a dynamic, rapid-approvalextension and update to various certificate mechanisms would also needto be employed. Another, less forgiving, example might be a detection ofa route deviation to a party. Going to a party, with an arm/basket loadof groceries might be considered especially problematic (e.g., a risk ofnot only the person being exposed, but the groceries intended for therest of the household being exposed), and thus the actions employed mayhave a much shorter/limited set of actions trying to rectify theperson's course before law enforcement or other authorities are alerted.Or, other more “harmful” yet not-quite-as-bad-as-alerting-authoritiesmight be employed, such as social credit score negative hits or evenpublic shaming on social media. As an example of the use of interfaces,as the person follows an “approved” route, the navigation system mightuse one standard pleasant voice. But upon a deviation of the route, thevoice changes, for example, from a woman to a man's voice that getsprogressively angrier and/or louder as the deviation continues/getsfarther from the approved route.

Upon entering a building (e.g., for apartment dwellers, etc.), specificconditions of the building and context of the person could come intoplay. For example, a problem in many older buildings is that the maindoor out of the elevator must be opened by hand, and apparently it isagainst the fire code to put an automatic opener. Anyone going in or outof the building must touch that door handle and pull. This is obviouslyproblematic in many respects, so as the person nears the building, anappropriate resource must be employed to operate the elevatorappropriately, both to prevent the person from having to touch it (e.g.,have less people touching anything on the elevator) as well as in theprocess assist the person with the groceries. Exemplary embodiment(s)would ensure that this took place appropriately, particularly from atiming and social distancing standpoint. This “narrow” assistance couldalso take into account making sure the steps up to the building werecleared of ice and snow under a presumption that such regularmaintenance of such would be discouraged and thus need to be done forexemption travel only in a just-in-time basis.

Upon returning home, a variety of sensors and actions would then beemployed. Based on individual goods, sensors in storage spaces, theshopping list, the shopping list receipt (e.g., actual goods), and/orall of the above, the household inventory of goods would be updated tobe used in future risk of violating quarantine calculations. Tracking ofany support resources who entered the household would be noted, and theexemption certificate “closed” or otherwise updated to show that theexemption was successfully used and within the allotted conditions.

In addition to (or an expansion on) the capabilities exemplified by thevirus/grocery store examples above, another overall distinction betweenthe exemplary embodiments disclosed herein and prior art, is that whilesome prior art may be considered a “warning system,” or, at best, evenan “early warning system,” exemplary embodiments disclosed herein are an“extra early-early warning system.” In the virus/grocery store examplesdisclosed herein, all sorts of risks may be assessed and incorporatedinto the broader purposes disclosed for exemplary embodiments. Forexample, the risk(s) of running out of food or key supplies, toindividual health needs, that might lead to a temptation to violate thequarantine could be assessed and preempted. On the other end, adeviation from a route (in consideration of other factors, such aslikely destinations, time left over before the authorization expired,how much he was carrying, etc.) could be assessed for its likelihood ofthe deviation to be accidental versus deliberate, with context-based,even dynamically created actions (and resources andintegration/interfaces, as applicable) being implemented to prevent anactual violation from occurring, well before it could occur, thus(hopefully) avoiding all manner of actual violation-induced punitivepenalties and enforcement actions. Thus, the distinction between “earlywarning” (at best) of various prior art, and “extra-early” early warningsystem as disclosed herein is not just a question of semantics; instead,it is a major difference that is reflected in exemplary embodimentsdisclosed herein that are greatly distinct from prior art, and theassociated emphasis in exemplary embodiments on preventing (e.g., doingessentially everything possible to prevent, etc.) an actual violationfrom occurring, through extensive use of dynamic, context-based,utilization of sensors, risk evaluation mechanisms, dynamic use ofactions, resources, and interfaces, and the use of and feedback into theinvention of actions et al. learnings via Artificial Intelligence,machine learning, and other learning mechanisms.

Exemplary embodiments may also be configured for monitoring forand/detecting possible illness(es) based on changes in behavior. Whilethis would include monitoring/detecting changes in temperature-fevers,sluggishness, etc., exemplary embodiments may be further configured tomonitor for and detect unusual and/or non-medical behavior, such as, forexample, changes in voice frequency, changes in sleeping habits, orperhaps decreased “productivity” from everything from how fast they canprepare a meal to how fast they type, as compared with historical(contextual) norms. The above in particular would be context-based, suchas work typing (in Word) vs. doing personal texts. Advantageously, suchexemplary embodiments may thus be able to detect illness in persons whoare otherwise asymptomatic by detecting changes in behavior even if thepersons otherwise “feel fine.” This would involves detecting (possible)“extra early” early warning signs of an (eventual) medical situation ofasymptomatic cases and/or cases where eventually the persons get sickand have symptoms.

Exemplary embodiments may also be configured for monitoring behaviorswithin the home environment. For example, this may include monitoring todetermine whether school-age student(s) are studying (or not), along thelines of monitoring rates-of-consumption, etc. This may include trackingat-home schooling with the risk of the student avoiding required schoolactivities, starting with logging in to the school's daily classwork,etc. This may include monitoring computer time to distinguish betweenplaying video games versus school-related activities. The “risk” ofviolating quarantine associated with this would be that to the extentthe child is falling behind at school, the parent might be concernedenough to violate quarantine in some way to “break” it for somereasons—particularly if schools are in session for those “immune”children, and the parents get panicked enough about the student sopoorly with online learning that they want to break quarantine and sendhim to school, even though the student is not eligible.

Exemplary embodiments may include the concept of “tiered” quarantineareas. Thus, for certain locations, people, and/or context, there may bedifferently defined quarantine areas (and/or contexts). Thus, for agiven household, there may be multiple quarantines: some people,(possibly, even likely) further qualified by context, might beconstrained to the house or apartment (like seniors); other(people)/contexts to the yard (or for apartment dwellers-floor orbuilding); others let outside the home and lot/building but constrainedto the neighborhood or city limits.

Beyond this location-based geofence-type quarantine, these tieredquarantines could depend on context, e.g., a home dweller allowedoutside for chores (but not socializing), or the apartment dwellerallowed to go to the basement where the washer/dryers are to clean theirclothes (in this the “tiered” exemption may be “micro” in nature, e.g.,making sure than only one person goes down to the basement at any giventime). Again, this may all be geared to avoiding the risk of violatingquarantine. For example, if someone has a sewer problem that he thinkshas to be unclogged via the outside access, he will violate a house-onlyquarantine.

Exemplary embodiments may include location-less context-based “fence(s)”without any location element. For example, if a hurricane isapproaching, and a household is in need of plywood, certain foods, etc.,then a shopping-for-hurricane exemption can be granted, that doesn'thave any restriction on WHERE he can go, just WHY he is going, and WHAThe is getting, and perhaps how (e.g., has to go by car). A buildingdweller might have an exemption to walk her dog—not specifying exactlywhere or how (or maybe not even where); just that she's got a window totake her dog out, and perhaps being back by a certain time. Anotherexample is the clogged sewer in that it may be considered an emergency,and he (and the system of the exemplary embodiment) doesn't have time tofigure out where he needs to go to get what he needs, therefore he isgiven an emergency “sewer-unclogging” exemption that allows him to goanywhere (within a certain time limit perhaps), as long as it is for thepurpose of getting what he needs to fix his sewer problem.

In addition, the pooled resources aspects disclosed herein may beextended to other exemplary embodiments. For example, a building mayhave a specific common issue (risk) that needs to be addressed in apooled-related set of ways of detecting/monitoring it, evaluating therisk, and via pooled/common actions/resources/interfaces. This aspect ofpooled resources may be extended to location-less context-basedfence(s). For example, if the whole building has a sewer, water, orelectricity problem, and time is of the essence, then a commonpurpose-based exemption could be given to multiple people in the sametimeframe, without a number of restrictions that would typicallyaccompany an exemption (e.g., destinations, routes, etc.)

Regarding the interfaces, exemplary embodiments may preferably useinterfaces that (a) are contactless or those that at least minimizecontact with surfaces and/or nearness with other persons, and/or (b) donot require persons to remove their PPE, particularly gloves and masks.This would also include at key points, such as checking out at a store,e.g., avoiding all contact with anything through the use ofwireless-enabled mobile payment mechanism, as well as maintaining socialdistancing (e.g., air “contact”).

Exemplary embodiments may include the use of robotics/automation. Forexample, home delivery/delivery assistance via the use of (sanitized)robots, including interfacing with support resources/delivery drivers,with the goal of a) minimizing human contact, both with the groceries aswell as each other), and b) providing key support (instead of humansupport) to, for example, get the groceries from the front of thebuilding to inside the household unit. It would be pointless, forexample, to go to great extremes to buy the groceries only to latercontaminate the groceries getting them into the building, up thestairs/elevator, to and into the front door. This may also includerobotic bagging of groceries, or the “picking” of goods off shelves,e.g., instead of touching a good (and thus contaminating it,particularly if you put it back), the shopper could speak the good and arobotic arm would take it off the shelf and put it in the shopper'scart. Another option is to have the shopping list used as the pickingmechanism, e.g., the list is communicated to the (distributed orserver-based) shelf “intelligence,” and as the shopper approaches thegood a robotic arm has the good ready to put into the shopper's cart(which the shopper could reject via voice command, want 2 of theminstead of 1, etc.). Also, once this is done, then there may be anautomatic updating of the shopping list, which could be used as anothermechanism for making sure the shopper does not leave the store withouthaving bought everything that is needed (e.g., the reason the shopperwas granted an exception in the first place).

Accordingly, disclosed herein are exemplary embodiments of systems andmethods for monitoring for and preempting the risk of a futureoccurrence of a quarantine violation, such as by using behaviors and/oractions (e.g., pre-identified behaviors, preemptive actions, etc.)determined via one or more different devices, sensors, sensor arrays,and/or communications networks (e.g., the Internet of Things (IOT),social networks, etc.). Exemplary embodiments may include one or more(or all) of the follow major innovations not present in the prior art:

-   -   use of systems and process integration to preempt the risk of a        future occurrence of a quarantine violation    -   using context and being dynamic as compared to prior art systems        that are location-only, using “fixed” or static sensors, risk        algorithms, and actions. In contrast, aspects of the present        invention use context in some way as well as emphasizing        “dynamisms,” e.g., emphasizing rapidly changing risks,        behaviors, etc.    -   use of closed/partially closed loop and/or learning systems and        methods. For example, the system may continually learn and        improve without manual input the longer it goes on. The        system/may start with a pre-identified risk profile, associated        initial set of sensors et al., and initially identified risk        assessment algorithms (which, it should be noted, is not just a        score, range of values, etc., but can be in other forms, such        software code (this might be new), Qualitative descriptions        etc.), and initial set of actions, resources and interfaces.        Then, as the RESULTS of the actions/resources/interfaces are        measured, the results, via various methods and mechanisms        including AI, machine learning, etc. are then “fed back” (via        the “closed loop”) into the exemplary embodiment(s), and/or new        elements (e.g., sensors, actions, data sources) are determined        to need to be added (e.g., partially closed loop), including        updating the profile and/or also changing (possibly) the risks        measured; what sensors et al. are used and/or what        configurations/values are used/measured; what risk assessment        formulas/algorithms are used; and/or what actions, resources,        and/or interfaces are employed to address the (possibly now        modified) risks—particularly the context. Then, these results        are fed back again in an ongoing process of continual        improvement/modification/learning.

As disclosed herein and shown by the table below, triggers mayencapsulate various forms of mental thoughts, mental and/or physicalbehaviors, mental and/or physical states, and/or mental and/or physicalactivities. Generally, triggers are predicated on, or a description of,behaviors/activities/states which may be either or both physical and/ormental. By way of example only, the table below categorizes variousexamples of triggers in which “X” and “m” respectively indicate majorand minor applicability.

TRIGGER MENTAL PHYSICAL BOTH/Occurring Together Anger X Anxiety XBoredom X Change X X X (e.g, moving to new city) Depression X Escape X mm (e.g., getting away (e.g., feeling (e.g., getting from “everything”)physically closed in) out of house) Excitement X Fear X Fun XFrustration X Guilt X Health X X X (e.g., physical health problemscaused by mental conditions) Insomnia X m (e.g., too much caffeine) JobX m m (e.g., job may be physically exhausting) Kids X X X Loneliness X mCould be both - mental (e.g., Loneliness loneliness caused by lack canoccur even when of physical interaction) other persons are around) MoneyX X Mid-Life X Overconfidence X Peer Pressure X Power X Powerlessness XProximity X Fear of Quitting X Relationships X X X Relatives X m m(e.g., Political differences w/relatives may be huge, even if youhaven't seen them in years) Sex X (desire) X (actual) X Smell X Stress Xm m Taste X Times of Day/Holidays X UnFun X Victim X Weather m X Fear ofstorms is both mental (e.g., Possibility of and physical being snowed-inbefore snow even starts is mental ex-relationship X - problems with X Xpartner exspouse, significant other, or other relationship partnerdoesn't necessarily require physical interaction Yelling/conflict X X X(e.g., fear of conflict (e.g., Physical is mental) yelling)

As shown by the table above, Proximity may be considered a physical onlytrigger, whereas Anxiety and Boredom may be considered mental onlytriggers. Some may be considered as both physical and mental triggers,such as money or relationships. In addition, a mental trigger (e.g.,Fear, etc.) may be caused by any sort of context, including mental(e.g., an imminent important deadline), physical (e.g., a physicalexamination that could determine whether I pass and can get the job), orboth physical and mental and physical (upcoming physical interview witha prospective boss). In addition, physical environmental factorsdisclosed herein (e.g., nature of lead pipes in a residential building,etc.) may be characterized differently than simply running out of anitem. Such environmental factors may be characterized as contributorycontextual factors (both physical and mental) that could be combinedwith triggers, e.g., mental/mentally-related triggers. Accordingly,triggers may thus be mental, physical, or a combination of both, whilecontext may also be physical, mental, or a combination of both (e.g.,being in a pressure-packed meeting at a physical workplace versus apressure-packed zoom meeting or upcoming deadline).

In addition, physical triggers may also be caused by and/or areinterrelated with mental triggers. For example, a person being hot(physical trigger) may be caused by elevated blood pressure (physical),which elevated blood pressure may have been caused by Anxiety/Worry(mental). As another example, a person feeling thirsty (physicaltrigger) may be caused by the person being hot (a both mental andphysical state), which, in turn, may have been caused by Anxiety/Worry(mental state/behavior/trigger). As yet a further example, a personbeing short-of-breath (physical trigger) may be caused by Anxiety orExcitement (mental). In addition to these examples, another exampleincludes mental and physical triggers that have a more clear/majorcause-and-effect connection, such as a person having mentalissue/trigger/state caused by a vitamin D deficiency and the householdis running out of vitamin D supplements/food.

Disclosed herein are exemplary embodiments of block-chain based dynamicand adaptive systems and methods for behavioral modification andrewards. By way of background, behavior modification programs have beenaround for decades, as have such programs with rewards associated withachieving behavior milestones or otherwise providing some sort offinancial/material incentive for modifying one's behavior. For example,U.S. Pat. No. 6,039,688 (hereinafter the '688 patent) is titled“Therapeutic Behavior Modification Program, Compliance Monitoring andFeedback System”. The '688 patent discloses a rewards program formodification of behavior relating to a wide variety of ailments,including heart disease, stroke, diabetes, asthma, chronic pain,depression, addiction, cancer, and a wide variety of other ailments. The'688 patent discloses that the changed behavior includes changes todiet, exercise patterns, and stress levels. Other patents minor thescope of the '688 patent, generally adding various “bells and whistles”as to how the modification program and/or compliance monitoring/feedbacksystem is implemented.

The '688 patent, and others like it, have a variety of commoncharacteristics, notably that many if not all of their key componentsare fixed in terms of the component either having to be predefined(e.g., before a behavioral modification rewards program can beestablished and put in operation) or at least be narrowly defined,structured and implemented before providing or awarding any reward.Examples of how the key components are fixed and predefined include:

-   -   The behavior(s) that are the basis of the modification program        and associated rewards need to be predefined.    -   The modifications to the behavior and/or associated goals need        to be established/predefined.    -   Milestones associated with the behavior(s)/behavior modification        need to be established/predefined.    -   Actions/feedback mechanisms to encourage behavior        modification/achievement of milestones need to be        created/predefined, as well as conditions to initiate such        actions/feedback mechanisms.    -   Quantification and associated measurement of the        behavior/behavior modification/milestones/actions/feedback        mechanisms need to be quantified and translated to measuring        mechanisms that need to be predefined.    -   Rewards programs, reward types, levels of reward within type,        range/allowable conditions for an award to be awarded, values of        any given reward, the mechanism(s) for awarding a reward, and        the mechanisms for redeeming a reward need to be predefined.    -   Personalization or customization of any of the above needs to be        established/predefined.

In short, existing prior art fully or predominately requires some oreven all of the above elements to be predefined. These and other priorart characteristics, at a more granular level of detail, include in fullor in part:

-   -   A “fixed” reward system that includes specific milestones for        the person (such as an addict) that need to be met for a given        behavior and associated targeted/desired behavior modification.    -   Behavior(s), and associated behavior modifications, need to be        predefined, such as behavior=eating, behavior modification=eat        less, and/or goal=lose weight.    -   More specifically, these milestones, while by their nature        having some degree of personalization (e.g., Joe you need to cut        your drinking in half by next week, or Jane target losing 10        pounds by end of the month) are still generally fixed in terms        of the core behavior/activity (e.g., drinking, eating less) to        be changed. Further, as indicated by these examples, these        milestones by their nature also tend to have fixed time-related        elements, e.g., achieving a milestone by a fixed time, or within        a fixed time period, or other fixed temporal metric, that may        (or may not) be also be personalized, e.g., Jane wants to lose        the weight by her sister's wedding on March 15th. Further, the        personalization is limited to the degree of change relative to        the fixed core/end behavior to be modified, versus related,        contributory, and/or underlying root cause behaviors (unless        those are also pre-defined).    -   Even when the milestones/behavior modifications/goals are        personalized, the rewards still tend to be fixed, or at least        not personalized in how the rewards are delivered. Meaning, even        if the reward/reward value is personalized to the person, the        presentation or personality is not. In other words, in the prior        art the reward tends to be a material or financial thing that        typically shows up as a credit in some account, or through some        sort of physical or virtual offer for free or discounted stuff.        Little attention is paid to how the user is informed/made aware        of their reward, e.g., there is no focus on the presentation or        personality associated with the reward delivery and        notice/information to the awardee.    -   Indeed, the nature of prior art rewards tends to be physical or        financial. There is little to no attention paid to rewards in or        involving the virtual world, such as the awarding of game tokens        or other game-related add-ons as it relates to behavioral        modification rewards. Thus, the delivery mechanism of the reward        is relatively fixed in terms of form/form factor.    -   The prior art fixed reward systems are fixed not only with        respect to specific milestones and delivery mechanisms, but also        require that specific rewards/level of rewards for specific        behavior modification be determined beforehand, e.g., receive        $10 in discount coupons for every 1 pound lost. More broadly,        there are no mechanisms for dynamically/adaptively creating,        implementing, and delivering new rewards programs/rewards/levels        “on-the-fly,” or dynamically adapting a program to reflect        changing conditions/circumstances/contexts, or even        monitoring/measuring/rewarding “new” behaviors related (or not)        to the original “pre-identified” behaviors. Instead, nearly all        rewards programs, if modified at all, are done so program-wide,        typically after deep proforma analysis of how such changes would        impact the financial and operational viability of the system as        a whole. To the extent such changes are made dynamically, they        are done across-the-board with respect to program participants,        not individually to reflect changing        behaviors/circumstances/contexts of the person. For example, for        a person with the Anger trigger, the ability to manage their        anger may be vastly different (and more difficult) for them        while at work, versus by themselves or at home. This variation        in susceptibility to anger based on these different contexts may        not be known when the (original) behavior modification program        was set up—with such a variation (in the prior art), if        recognized at all, needing to be (pre)established in a new        behavior-to-be-monitored profile well after the fact of such        discovery—not dynamically/near instantaneously. In these “newly        discovered” contexts, it will be more important to reward—near        instantaneously, or at least very rapidly—“good” behavior (e.g.,        managing the underlying causes of and/or contributors to his        anger), at work and/or around other people, vs. when he is        alone/at home. Thus, the ability to dynamically change/adapt the        rewards program to a wide variety of personalized, rapidly        changing, and/or newly/recently discovered (typically)        context-based dimension(s) is absent in the prior art.    -   Further, once a reward is awarded, these prior art systems also        have fixed redemption elements (e.g., redeem this coupon within        1 year) that does not vary between persons to which it is being        awarded. In other words, these prior art systems typically lack        personalization in terms of redemption conditions and/or        processes, or they are limited to time-related dimensions, e.g.,        within one year of being earned, by his/her next birthday, etc.    -   The behavior to be modified is predominately physical, e.g.,        drink less, eat less, etc. Further to the extent that there is        additional behavior modification to be tracked/monitored and        modified, this related behavior is closely and/or directly        related to the core behavior. For example, “drinking less” may        be more specific in terms of “drink less wine,” but not        involving any “upstream” behaviors that may lead to drinking        more (or less), whether it's wine or any alcohol.    -   These “upstream behaviors/contexts/situations/circumstances”        also referred to herein as “triggers” are absent in the prior        art. Instead, the prior art is focused on the end behavior not        any upstream behavior or triggers. For example, the prior art        may focus on reducing drinking rather than recognizing (and        trying to prevent) that Anger is a major trigger for his/her        drinking (e.g., he/she drinks when angry). This prevents        monitoring of upstream        behaviors/contexts/situations/circumstances that would allow for        “pre-emption” of the end/core behavior. To the extent the prior        art does include trigger-like behaviors, the trigger-like        behaviors are generally subordinated in the prior art to some or        all of the fixed characteristics described above thereby        excluding the ability monitor/detect/establish actions/create        rewards dynamically to        behaviors/contexts/situations/circumstances that were heretofore        unknown to contribute to his/her anger or reduction of anger.    -   The actions associated with modifying the behavior, if based on        monitoring, are also physical, e.g., monitoring your diet        indicates you are eating too many carbs and should eat more        fruit next week.    -   Tracking behavior modifications, to the extent the prior art        uses sensors and other electronic mechanisms, are focused on        tracking the specific end, or core behavior, such as losing        weight or drinking less. In this sense, the prior tracking        mechanisms are also fixed and focused on tracking a specifically        defined behavior or limited set of behaviors. The prior art does        not include a broad range of tracking mechanisms nor mechanisms        that can adapt to changing/new upstream        behaviors/activities/situations/circumstances/contexts not        originally anticipated when the rewards program and associated        actions were created.

In general, the above translates, among other characteristics, toforcing the person to change their behavior to meet the requirements ofthe rewards system. In contrast, Applicant's systems and methodsdisclosed herein are configured to do the opposite as changes are madeto the rewards system/program(s) to meet the requirements,characteristics, and/or behaviors, activities and/or context(s) of theperson even to the extent of dynamically creating new rewards programswith new actions for newly identified behaviors needing to be modifiedrapidly.

Put differently, the prior art behavior-modification rewards programs donot have the following characteristics/attributes:

-   -   A “variable” reward system that adapts to the behavior of the        person, both in types of rewards and levels of rewards.    -   Adaptable, dynamic, or even no pre-set/fixed milestones for the        person to achieve. Rather, prior art rewards program(s) are        dependent upon the behaviors/change in behaviors (of any sort)        of the person.    -   Ability to dynamically/adaptively create and implement rewards        programs, rewards within a program, and levels with a reward        that are based on newly identified (e.g., not pre-identified)        behaviors, behavior modifications, and actions to enable/assist        in such modifications, and in effect be highly personalized and        adaptive within the entire end-to-end behavior modification        rewards process, even extending to the delivery        presentation/personality of how the reward is delivered.    -   Instead of just delivering a credit in an account, a free pass,        or a discount coupon (via email), a key (and neglected) part is        how a reward is delivered to the person. For example, for a        rewards program that is based on providing game tokens or        credits, some people may prefer a stoic artificially intelligent        (AI) avatar while other people may prefer a happy-go-lucky type        personality. So not only adapting the presentation, but also        adapting the User Interface elements that may possibly be        enabled by Artificial Intelligence/machine learning, etc.—can        facilitate positive change (or discourage negative        behavior/activities).    -   To the extent that there are significant degrees of        personalization involved in the prior art behavioral        modification programs, they tend to be focused on feedback        mechanisms to a person or support persons in the prior art. To        the extent these feedback mechanisms are truly personalized to        the person in some way, they are not personalized to the extent        of incorporating the context that the person/behavior of the        person is taking place within, nor the context of the        behaviors/behavior modifications, and, in turn rewards for those        behaviors/behavior modifications are taking place. Thus, if a        person has an Anger trigger, and that trigger is more likely to        be “tripped” or activated around other people in the workplace        (in stark contrast for example) with the person being alone at        home, which rarely triggers Anger, recognizing this context        becomes key to the entire rewards system: when, where, and how        the person's behavior is monitored and measured; what underlying        root causes/enablers/sources of possible anger need to also be        monitored and measured; what actions/feedback mechanisms are        provided to the person, support persons, or other support        mechanisms to help manage his/her anger in that context; what        elements are measured to verify success in that context;        deciding upon/creating (as applicable) the appropriate rewards        program, type, level, “personality,” and redemption process(es)        (and associated dimensions) for that behavioral improvement (or,        alternatively, “punishment” for “bad” behavior).

As recognized herein, utilizing virtual rewards through games or othervirtual activities should be added to the physical and financial rewardsof conventional rewards systems for behavior modification. It has alsobeen recognized it is desirable to have the ability to personalize theredemption conditions and/or associated processes that would allowpersonalization in to when, how, and under what conditions a reward canbe redeemed based on the needs and/or desires of the person and/or whatthe incentive system calculates or determines to be the optimal usagewindow/time/conditions for that reward relative to the needs of thebehavior modification reinforcement degree/type/level of behavior aswell as context, situation, and/or circumstance. For example, if thesystem detects that going for a walk in the park (in certain conditions,such as sunny days) is effective for lowering Anger-related biometriclevels (e.g., lowering blood pressure, etc.), an award of free passes tolocal parks may be accompanied by a personalized and dynamicallyestablished set of conditions such as being activated (with associatedalerts to the person) on sunny days in the locale in which the personlives/works.

Given the “fixed” nature of prior art reward systems, the “accounting”e.g., tracking of the person's adherence to reward-generatingbehaviors/behavior modifications in the prior art is relativelysimplistic due to the well-established, predefined variables andassociated reward-calculation algorithms. In contrast, the ability totrack dynamically and adaptively new/rapidly changingbehaviors/activities and associated contexts/situations/circumstancesbecomes much more complex, and associatively much more vulnerable to“mis-capture” of the reward-relevant behaviors and associated rewards.Accordingly, a need has been recognized herein for a new “accounting”system that is blockchain based.

Accordingly, disclosed herein are exemplary systems and methods thatinclude or use blockchain (e.g., cryptocurrency, virtual currency,digital currency) as a “platform” or management/accounting system for amanaging a “portfolio” of different dynamic/adaptive motivationmechanisms. For example, exemplary embodiments disclosed herein mayinclude a mechanism for managing—

-   -   Privacy (anonymization of data, but with ability to        “deanonymize” under the right conditions, e.g., persons and        keys).    -   Security (Prevent hackability: very important given the        incredibly invasiveness in terms of type and amount of data        being captured).    -   Both the Privacy and Security elements above would be part of        the blockchain's “Proof of Work” capability, where Proof of Good        or Bad Behavior is captured accurately and securely.    -   Adds/enhances mechanism to motivate persons (e.g., addicts,        parolees, etc.) to behave “good” recognizing that different        people (and even at different times) will be motivated        differently.    -   Adds a “smart contract” mechanism.

In exemplary embodiments disclosed herein, implementation of suchblockchain components may include using/enabling:

-   -   Distributed network(s)    -   Wallets/profiles for users    -   Use of smart contracts    -   Addition of Internet of Things (IoT) style sensors to validate        truthfulness, breathalyzers, location systems, other actors        verifying person is not drinking, etc.    -   Reputation of sensors/actors—built in reputation score to cross        validate the truthfulness of the other sensors—2 out of 3        breathalyzers agree—maybe the 3rd breathalyzer is faulty    -   A wide range of personalized dynamic/adaptive redemption        mechanism(s) such as: lottery, points/score, badges,        profiles/social media, discounts, redeem for goods/services,        etc.    -   Access portal    -   Immutability    -   Interoperable—not dependent on a single system or reward    -   Extendable—ability to add additional incentives/rewards    -   Can have reward and punishment, but incentive is on encouraging        good behavior    -   Structured data that allows for machine learning and analysis,        while maintaining the integrity and privacy of the individuals        and being in compliance with data privacy regulations, or        potential changes    -   Risk recognition—component that can see (e.g., using machine        learning) that the subject is rapidly in a downward spiral and        allow for some sort of “break the glass” style intervention        (e.g., a digital/real world parole officer, trauma consoler, or        a sponsor)

In exemplary embodiments disclosed herein, the behavior modificationrewards programs disclosed herein are not limited to actual “end” or“core” behavior milestones, such as losing 10 pounds or drinking 4 lessdrinks per day. Instead, and/or in addition, the rewards programs reward“upstream” behaviors that underlie the “end” behavior(s) to be modified.For example, if getting Angry causes a person to want to drink (an“Anger” trigger), the rewards program focuses on reducing/modifyingsituations/contexts that might lead to the person getting Angry. Thiskind of “preemptive” anger management (in this particular example) isvery distinct from what in traditional behavior modification programswould be focused on managing (e.g., calming down the person) once theperson is already angry.

In exemplary embodiments disclosed herein, the behaviors (upstreambehaviors and/or core behaviors) are not limited to only physical orphysically measured end behaviors. But the behaviors also include (viathe use of sensors or other mechanisms) mental “behaviors”, such asreducing Anxiety or pre-empting Anger, addressing or reducing thebehaviors, activities, situations, circumstances, and/or contexts, whichmay include thought processes that may cause or lead to an increase inAnxiety or lead to Anger.

In exemplary embodiments disclosed herein, the actions associated withthe behavior modification are also not limited to physically-orientedactions (e.g., drink less). Particularly because the actions are focusedon pre-emptive, “upstream” behaviors, including mental behaviors, theactions to take and the rewards to be granted once taken are alsofocused on pre-emptive, broad-based behaviors, which are not the focusof existing behavioral modification rewards programs.

Moreover, the actions to be taken and (dynamically) rewarded are notpredefined let alone fixed in exemplary embodiments disclosed herein.Instead, if the person undertakes an action (of any sort) that leads toa positive (core or upstream) behavioral impact, that heretofore“unknown” action and its benefits will be detected and with rewards toencourage future similar behavior dynamically created and calibratedbased on context as well as administered. For example, if the person hasa problem with the Anger trigger, then Anger (for that particularperson) may be determined or measured by specific levels of bloodpressure, voice volume, and skin temperature. In this example, if theperson decides to go for a walk in a quiet city park, and the sensorsfor one or more of those variables detect a positive movement (e.g.,lowering in this case of those underlying trigger values for bloodpressure, voice volume, skin temperature, etc.), the context “walking inthe park” could be dynamically added to the rewards programactions-to-be-rewarded list perhaps with some associated values (e.g.,in the park for at least 30 minutes). And, a reward may be dynamicallycreated to encourage future walking-in-the-park, such as free passes toprivate parks near the person's home or work and/or other parks withsimilar characteristics to the “successful” park, such as open duringcertain days/times, forested with lakes and walkways, pets allowed, etc.Other contextual elements associated with the “successful” walk in thepark may also be incorporated, such as it was a sunny day, cool but notcold, pets were nearby, etc. Accordingly, the reward could becreated/modified/calibrated to encourage future walks under similarconditions that if not incorporated into the pass, then (possibly also)incorporated into various future actions. For example, when anger againbecomes high-risk/a concern, the actions engine/rewards administeringengine could create alerts/notifications to the person encouraging(free) walks when it was sunny and cool, or (regardless of weather)encouraging the person to go to Park #C which happens to be the closestto the person at the time where pets are known to be popular.

To support the adaptive/dynamic nature of the above, exemplaryembodiments may include monitoring/tracking mechanisms configured totrack/detect a wide range of both mental and physical upstream behaviorsand activities/situations/circumstances/contexts. In such exemplaryembodiments, a much broader range of monitoring/tracking mechanisms maybe employed including ones that might not be immediately recognizable tobe relevant to monitoring/tracking the core end behavior(s).

Exemplary embodiments may include dynamically/adaptively new orheretofore “unknown” actions as well as new/unknown reward types and/orlevels. In which case, this dynamic/unknown nature may necessitatemachine learning, AI-type of dynamic/adaptive analysis and actioncreation that is absent in the prior art.

The rewards programs enabled by exemplary embodiments disclosed hereincan be wide-ranging, dynamic, and adaptively created thereby enablingrewards derived from a wide variety of behaviors. In such exemplaryembodiments, traditional managing/administering of dynamic/adaptiverewards by conventional mechanisms is problematic in terms of trackingand fraud prevention, among other issues. Towards that end, theutilization of blockchain technology to track, manage, and redeem suchdynamic/adaptive rewards is therefore employed in exemplary embodiments.

Some exemplary embodiments disclosed herein may adopt all of the newcharacteristics/elements disclosed herein. Generally, exemplaryembodiments disclosed herein include one or more (or all) of the followmajor innovations not present in the prior art:

1) emphasis on rewarding (or discouraging) “upstream” behaviors thatimpact the “core” behavior versus the core behavior itself, and with aparticular emphasis on pre-empting the end/core behavior;2) having the rewards program adapt to the behavior(s) of the person notvice versa;3) dynamically monitoring for, detecting, and adapting actions andassociated rewards to the person'sbehaviors/situations/circumstances/activities/contexts versus havingsuch behaviors/situations/circumstances/activities/contexts being“fixed” or “predefined” and, in turn, not having the associated actionsand/or rewards also being “fixed” or “predefined” but rather dynamic andadaptive; and4) an emphasis on dynamically creating/adapting rewards programs andunderlying actions via machine learning, neural networks, artificialintelligence, and/or other mechanisms that do not require humanintervention to create/define/manage/administer such rewards/actions.

Definitions and examples are provided below for various terms forpurpose of example and illustration only. For example, a “reward” as inthe traditional sense is “a thing given in recognition of one's service,effort, or achievement.” In prior art reward systems, such rewards areeither material (e.g., a physical thing) or financial (e.g., money). Butin exemplary embodiments disclosed herein, a “reward” can be manydifferent things beyond material/financial or beyond the commonconception of material or financial. This expansive definition of rewardincludes:

-   -   (Virtual and Physical) Coins, Tokens, and Currency (Usable,        Proprietary, and/or Cyber), monetary/account credits    -   Ribbons, Plaques, Degrees, Ranks    -   Coupons, e.g., discounted or free-stuff vouchers/coupons    -   Free (physical) stuff, e.g., goods and services    -   Free passes to events, places    -   Karma Profile;    -   Cybercurrency credit/debit card;    -   Game-related (e.g., loot boxes, additional tools, skills,        features, avatars, etc.)    -   Lottery tickets, numbers    -   Points and scores/stats sheets    -   Tax incentives/rewards    -   Insurance-based incentives/rewards    -   Healthcare-based incentives/rewards (e.g., every 2 months sober        get a “free” massage sponsored by healthcare provider);    -   Education incentives/rewards/rewards (e.g., “real” and online        training, vocational programs, etc.)    -   Time incentives/rewards/rewards (e.g., free use of a state        campground, or 2 weeks off for good behavior)    -   Fantasy games-related incentives/rewards/rewards    -   Avatar-based systems-related incentives/rewards/rewards    -   Specialized social networks where you can “brag” about        “achievements” (blockchain verifiable, cybercurrency based).

In general, rewards programs are focused on a specific, “fixed” behavior(e.g., drinking, eating) or a limited set/combination of behaviors orbehavior modifications (e.g., exercise more, eat/drink less, etc.) thatwill have “fixed” conditions (often with time elements), e.g., lose 10pounds in the next month, exercise for at least 30 minutes at leasttwice a week, drink on average 4 less drinks per day over the nextmonth, etc. In turn, these rewards programs focus on one or a (very)limited set of the above reward types, particularly physical/financialrewards (e.g., free goods and services, coupons/vouchers, monetarycredits, etc.). Within a type, the “conditions” by which a reward isawarded is also generally “fixed,” such as a coupon for X in the amountof Y (%), possibly along with various redemption conditions (e.g.,redeem by Z date). The nature of these “fixed” requirements, e.g., afixed reward type, the level, time constraints, etc. for a “fixed”behavior modification (e.g., lose 10 pounds by the end of next month) isgenerally consistent across all prior art rewards systems(behavior-focused and non-behavior focused, such as traditional airlinemile earnings programs) monitoring/modification.

The prior art has very significant fixed elements of at least one of:behavior(s) to be monitored and modified, actions and/or feedbackmechanisms, reward program types, reward levels, reward awardingconditions, and/or reward redemption conditions. Accordingly, it isrecognized herein that the prior art is missing or lacks the ability toprovide an adaptive, dynamic behavior modification rewards platform thatis centrally designed around broad-based, dynamic/not “pre-defined”behavior.

Although conventional behavior/activity modification encourage may existin a variety of fields and forums, these tend to be “on-offs,” e.g.,specially designed/targeted system to encourage specific behaviors oractivities, such as points-based rewards programs that accrue pointsbased on numbers, volumes, and/or dollar values of purchases in order toencourage more purchases. Other more personal behavior-oriented systemsare also typically specifically designed, e.g., encouraging “healthy”lifestyles. These personal behavior modification systems tend to focuson a specific behavior (e.g., eating/dieting) with a specific amount ofdata elements. Furthermore, such rewards programs all tend to be veryfocused at major problem activities or behaviors. Other prior art may bedesigned to discourage certain behaviors or activities, such as GPSAnkle-bracelets for persons on parole who know that if they traveloutside of approved zones (“geofences”) at all or at certain times thesystem will report them as violators potentially leading to the revokingof parole.

The prior art behavioral modification rewards programs and associatedsystems have the following characteristics:

-   -   The prior art focuses on the direct behaviors or activities to        be done (and rewarded) or avoided (or be punished). Thus,        alcoholism rewards programs reward a person for not drinking or        take away rewards for drinking.    -   The prior art does not address the modification of (and        rewarding for) “upstream” behaviors. Thus, most “healthy”        lifestyle programs/systems (even platforms) will, for example,        focus on eating healthy foods and regular exercise. But this        prior art does not address the “upstream” triggers, such as        Anger, Boredom, Relationship problems, etc., that might lead a        person to eat unhealthy foods. The prior art also does not        identify, monitor, and/or take action to remove key obstacles to        performing regular exercise, such as depression, spending (too        much) time with supposed “friends” that do not exercise or worse        “friends” that encourage bad habits such as hanging at the bar        drinking and eating fatty foods.    -   Prior art rewards programs/systems are built around “fixed”        types of rewards for “fixed” types of behaviors with little to        no personalization or context-based variation. For example, not        drinking for 30 days might reward you with a coin—there is no        variability or adaptability in the possible use of other reward        mechanisms based on the person's motivation factors and/or        context. In rewarding a coin, however, the prior art does not        address the challenges/difficulties that the person might have        encountered (e.g., the context), and there are no context-based        variations in the rewards given.

Exemplary embodiments disclosed herein illustrate and/or incorporateinventive features and elements described above. For example, anexemplary embodiment disclosed herein may involve an addict whosetriggers are Anger, Boredom, and Escape. The addict's anger level mightbe determined by monitoring three biometric elements: blood pressure(specifically high risk when the addict's blood pressure is over130/90), speaking volume (indicative of anger when the addict's speakingvolume is 5 or more decibels greater than the addict's normal speakingvolume), and skin temperature (high risk when the addict's skintemperature is over 10 degrees higher than the addict's normal skintemperature for a given context/activity). To the extent possible, thelocations, contexts, and/or activities that the addict was involved inthat caused, or at least were associated with, the increase in theaddict's anger level will also be determined. For example, if the addictwas driving while on the phone with the addict's mother when theaddict's anger level (as indicated by the biometric sensors) beganescalating, this exemplary embodiment may then theorize that the callwith the addict's mother and/or traffic issues (e.g., road rage) werethe likely culprits.

Given the high risk/trending risk of Anger occurring, combined with thetheoretical cause(s), this exemplary embodiment may encourage the addictto go to places and/or engage in activities to reduce rising Angerlevels (as indicated by the above risk factors/metrics). If the addictis successful, the addict is rewarded, and the type of reward and amountis determined by what he/she does, where he/she goes, and by how muchthe risks/metrics were reduced/successfully modified. If, for example,he/she was near a city park when the high-risk situation is flagged,this exemplary embodiment may suggest he/she go to somewhere quiet andoutdoors (away from his/her phone and car) and suggest a variety ofnearby parks/settings. If he/she goes to Park D, and in doing so reducesAnger, then the system will determine a type of reward and “level” of areward based on a variety of factors, including how successful he/shewas in reducing the risk, by how much, how quickly, and how “easily,” aswell as other personal motivating factors such as how little it cost,whether he/she took the recommended navigation path (that minimizedtraffic congestion and directed him/her to open parking spots, thuslimiting his/her road rage, with more reward if he/she followed theinstructions), etc. Thus, the reward in this exemplary embodiment isbased on what he/she does to reduce his/her anger and also how he/shedoes it.

In exemplary embodiments, the reward type/level/amount may also beadapted/calibrated depending on whether he/she was “killing two birdswith one stone” and also satisfying/addressing other triggers even ifthe person is not in a high-risk situation, such as the addict's Boredomtrigger. When the addict was Angry, the addict may have not necessarilybeen Bored. In exemplary embodiments, the reward type/amount/level mayalso be dependent on how enjoyable the activity was, e.g., did theaddict (broadly, the person) have fun or otherwise benefit from the tripto the park above and beyond the success in lowering anger level.

Some exemplary embodiments may include providing game tokens or virtualavatars as rewards, improving online behavior, skiing as a good/badbehavior tracked via beacons without requiring phone usage, proactivelyidentifying actions (for future use and for rewards) byobserving/monitoring effect on addict, etc.

As indicated above, exemplary embodiments disclosed herein include anovel and innovative feature that is the reversal of the person—rewardsprogram dynamic. Instead of the rewards program “dictating” what theperson should do (or should not do) to earn rewards, the person is ineffect dictating what kind of rewards he/she should receive and underwhat conditions. Not only does this enable a variety of benefitsincluding dynamically and adaptively changing what types (and whatvalues) of rewards are rewarded, but also the conditions under which therewards occur are fluid. Plus, the rewards are not necessarily orrequired to be known ahead of time in some exemplary embodiments.Moreover, new rewards can be dynamically created with exemplaryembodiments if the user's behavior and context necessitate/enable suchnew rewards.

Exemplary embodiments may be configured to use AI, such as using AI toexamine the blockchain (or any data/data set) trail to identifybehaviors/behavior patterns not necessarily picked up (e.g., picked updirectly) by sensors. For example, an exemplary embodiment may includethe AI observing and/or examining the blockchain itself to makepredictions for identifying behaviors/behavior patterns.

As recognized herein, there are a multitude of human conditionsrequiring new ways of modifying and/or rewarding associated behaviorswith those conditions as existing ways of modifying the associatedbehaviors have met with limited success, have high costs, and/or haveheretofore been limited in how technology can be applied. Three suchexample conditions are: addiction, incarceration, and parole.

In an exemplary embodiment, a system comprises a plurality of differentdevices, sensors, sensor arrays, and/or communications networks. Thesystem is configured to determine, through a plurality ofmeasurements/readings taken by the plurality of different devices,sensors, sensor arrays, and/or communications networks and/or inferredthrough information from system inputs, behavior(s) of at least oneentity and context(s) associated with the behavior(s) of the at leastone entity. The system is further configured to: dynamically andadaptively determine a reward for incentivizing behavior and/or contextof the at least one entity and facilitate redemption of the reward;and/or dynamically and adaptively determine a disincentive fordisincentivizing behavior and/or context of the at least one entity andfacilitate redemption of the disincentive.

In an exemplary embodiment, the system is configured to: determinewhether at least one trigger is active or present based on thebehavior(s) of the at least one entity and the context(s) associatedwith the behavior(s) of the at least one entity, as determined throughthe plurality of different devices, sensors, sensor arrays, and/orcommunications networks and/or inferred through information from systeminputs; determine a reward for incentivizing behavior and/or contextthat caused or created the at least one trigger to be active or present;and facilitate redemption of the reward. The at least one trigger maycomprise at least one positive trigger including one or more of anaspiration trigger, a goal trigger, a happiness trigger, or otherpositive trigger associated with behavior (e.g., good behavior, etc.) tobe incentivized and encouraged by the system.

In an exemplary embodiment, the system is configured to dynamically andadaptively: determine a reward for incentivizing at least one positivetrigger associated with behavior (e.g., good behavior, etc.) of the atleast one entity; and facilitate redemption of the reward to therebyincentivize and encourage the behavior of the at least one entity. Theat least one positive trigger may comprise one or more of an aspirationtrigger, a goal trigger, a happiness trigger, or other positive trigger(e.g., do-right-by-the-world trigger, be a better person trigger, doright by family trigger, be a good parent trigger, be a charitabletrigger, attain spiritual growth/religious values trigger, desire toserve the community trigger, a desire to attain the “good”, pay olddebts, etc.) associated with the behavior to be incentivized andencouraged by the system.

As disclosed herein and shown by the table above, triggers mayencapsulate various forms of mental thoughts, mental and/or physicalbehaviors, mental and/or physical states, and/or mental and/or physicalactivities. Generally, triggers are predicated on, or a description of,behaviors/activities/states which may be either or both physical and/ormental. By way of example only, the table above categorizes variousexamples of triggers in which “X” and “m” respectively indicate majorand minor applicability. Although some triggers may be considerednegative triggers (e.g., anger, anxiety, boredom, depression, fear,frustration, etc.), other triggers may be considered as positivetriggers. Example positive triggers may include aspiration triggers,goal triggers, happiness triggers, do-right-by-the-world triggers, be abetter person triggers, etc., which positive triggers may be encouraged,rewarded, and/or incentivized in exemplary embodiments disclosed herein.Accordingly, exemplary embodiments may be configured to not onlydiscourage bad behavior but also to inspire, incentivize, and rewardentities (e.g., persons, etc.) to good behaviors. For example, manypeople volunteer at food banks or habitat for humanity simply because ofthe desire to help the world without any expectation of a reward. Inthis example, a system may be configured to reward such the goodbehavior even if it was just providing a report of the volunteer hoursworked and an occasional anonymous coupon, etc.

By way of further example, a positive trigger may be based on a a notion(e.g., Aristotelian view on morality, etc.) that overall people tend tobe kind of neutral. A person(s) may be less bad in some ways but alsomore good in other ways for which there are different thoughcomplementary motivations for each. For example, a person may want tostop drinking alcohol because it has ruined his/her social life, so theperson may try and overcome the negative motivations that initiallyexacerbated it (e.g., angry with parents, boredome, etc.). In thisexample, the person may be able to obtain and maintain sobriety via asystem/method disclosed herein. While the sobriety may stop furtherdegeneration of the person's relationships with others, the sobriety inand of itself may not actually repair the person's relationships withothers. In which case, the person may need to embrace more positivemotivations, e.g., do right by family trigger, be a good parent trigger,be a charitable trigger, attain spiritual growth/religious valuestriggers, a desire to serve the community trigger, a desire to attainthe “good”, pay old debts, etc.

In addition, exemplary embodiments may include negative zones that aredisincentivized or restricted and/or positive zones that areincentivized or rewarded. For example, a negative zone restriction mayinclude a person being at a bar or casino, whereas a positive zone mayinclude the person spending time at the gym or with family.

In exemplary embodiments, the at least one entity may comprise one ormore of a person, a robot, an artificial intelligence, an animal, avirtual agent (e.g., video game virtual agent, etc.), a corporation, abusiness entity, a nation, a network, and/or a governmental entity. Theincentivized behavior and/or context may include one or more of asustained behavior, an improved behavior, an environmental contextchange, and/or a change of circumstances of the at least one entity. Thedisincentivized behavior and/or context may include one or more of adiscouraged behavior, an environmental context change, and/or a changeof circumstances of the at least one entity.

The system may be configured to use blockchain or other distributedledger technology (DLT) to track, manage, and redeem reward(s) and/ordisincentive(s).

The system may be configured to dynamically and adaptively: determine anew reward for incentivizing behavior and/or context of the at least oneentity, which new reward is not predetermined, predefined, or fixed inthe system; and/or determine a new disincentive for disincentivizingbehavior and/or context of the at least one entity, which newdisincentive is not predetermined, predefined, or fixed in the system.Additionally, or alternatively, the system may be configured todynamically and adaptively: determine a reward that is predetermined,predefined, or fixed in the system for incentivizing behavior and/orcontext of the at least one entity; and/or determine a disincentive thatis predetermined, predefined, or fixed in the system fordisincentivizing behavior and/or context of the at least one entity.

The system may be configured to dynamically and adaptively determine thereward and/or the disincentive by using one or more of machine learning,a neural network, a quantum network, and/or an artificial intelligence.For example, the system may be configured to dynamically and adaptivelydetermine the reward and/or the disincentive without requiring manualhuman intervention to create, define, manage, and administer the rewardand/or the disincentive by using one or more of machine learning, aneural network, a quantum network, and/or an artificial intelligence.

The system may be configured to dynamically and adaptively determinerewards and/or disincentives by adapting types and levels of the rewardsand/or the disincentives to the behavior(s) and associated context(s) ofthe at least one entity.

The system may be configured to use a proof blockchain system or otherdistributed ledger technology consensun mechanism for privacy protectionand security. For example, the system may be configured to use aproof-of-work (PoW) blockchain system and/or a proof-of-stake (PoS)blockchain system for privacy protection and security.

The system may be configured to use a blockchain system or otherdistributed ledger technology for data anonymization. The system may beconfigured to use one or more blockchain components including at leastone of a distributed network, a wallet, and/or a user profile. Thesystem may be configured to use artificial intelligence to observe andexamine a blockchain trail to make predictions identifying behaviorsand/or behavior patterns.

The system may be configured to determine a blockchain verifiable and/orcybercurrency based reward for incentivizing behavior and/or context ofthe at least one entity and facilitate redemption of the blockchainverifiable and/or cybercurrency based reward.

The context(s) associated with the behavior(s) of the at least oneentity may include a physical and/or virtual location of the at leastone entity and a context of the at least one entity at the physicaland/or virtual location.

The system may be configured to utilize blockchain or other distributedledger technology to capture transactions of the at least one entity andto retroactively determine and facilitate one or more rewards based onand/or associated with one or more of the transactions of the at leastone entity captured by the blockchain or other distributed ledgertechnology.

The system may be configured to utilize blockchain or other distributedledger technology to capture transactions of the at least one entity andto provide data to and/or obtain data from one or more other rewardprograms which data is based on and/or associated with one or more ofthe transactions of the at least one entity captured by the blockchainor other distributed ledger technology.

The system may be configured to utilize blockchain or other distributedledger technology to capture transactions of the at least one entity andto obtain data from one or more other reward programs which data isbased on and/or associated with one or more of the transactions of theat least one entity captured by the blockchain or other distributedledger technology. The system may be configured to identify one or moretriggers, actions, and/or patterns using the data obtained from the oneor more other reward programs.

In exemplary embodiments in which the system is configured to utilizeblockchain or other distributed ledger technology to capturetransactions of the at least one entity, the system may allow for easierintegration with other rewards programs. For example, the transactions(e.g., experiences, activities, etc.) of the at least one entitycaptured by the blockchain or other distributed ledger technology may besold or otherwise transferred to other rewards programs (e.g., to earnpoints, frequent flyer miles, etc.) without necessarily being requiredto directly sign up for the other rewards programs. This may also allowpast/existing programs to draw data into an analysis engine to doanalysis on how the participant was living life in the past, which maybe useful in for example identifying triggers, actions, patterns, etc.

The system may be configured to determine customized rewards andcustomized disincentives tailored to the behavior and/or associatedcontext(s) by the at least one entity. Accordingly, this may allow theat least one entity (e.g., person, other participant, etc.) to be thecenter of the rewards program(s), which may be a customized and/orindividualized rewards program(s), e.g., the rewards are tailored toreflect how a person lives his/her life, etc. In contrast, conventionalrewards program(s) are set up to be the center around which persons musttailor or change their behaviors, activities, lives, etc. to earnrewards.

In an exemplary embodiment, a method comprises determining, via aplurality of measurements/readings taken by a plurality of differentdevices, sensors, sensor arrays, and/or communications networks and/orinferred through information from inputs, behavior(s) of at least oneentity and context(s) associated with the behavior(s) of the at leastone entity. The method further comprises dynamically and adaptivelydetermining a reward for incentivizing behavior and/or context of the atleast one entity and facilitating redemption of the reward; and/ordynamically and adaptively determining a disincentive fordisincentivizing behavior and/or context of the at least one entity andfacilitating redemption of the disincentive.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprising computer-executable instructions, which when executedby at least one processor, cause the at least one processor todetermine, through a plurality of measurements/readings taken by aplurality of different devices, sensors, sensor arrays, and/orcommunications networks and/or inferred through information from systeminputs, behavior(s) of at least one entity and context(s) associatedwith the behavior(s) of the at least one entity, and cause the at leastone processor to: dynamically and adaptively determine a reward forincentivizing behavior and/or context of the at least one entity andfacilitate redemption of the reward; and/or dynamically and adaptivelydetermine a disincentive for disincentivizing behavior and/or context ofthe at least one entity and facilitate redemption of the disincentive.

In an exemplary embodiment, the system may be configured to assess,evaluate, and predict a risk of a future occurrence(s) of a behavior(s)and associated context(s) by the at least one entity. In such exemplaryembodiment, the system may be configured to: dynamically and adaptivelydetermine the reward to incentivize behavior and/or context that lowersthe risk of a future occurrence(s) of the behavior(s) by the at leastone entity before the behavior(s) occurs; and/or dynamically andadaptively determine the disincentive to disincentivize behavior and/orcontext that increases the risk of a future occurrence(s) of thebehavior(s) by the at least one entity before the behavior(s) occurs.The system may be configured to: dynamically and adaptively determinethe reward type, level, and amount based upon how, where, and by howmuch the risk was reduced by the incentivized behavior and/or context;and/or dynamically and adaptively determine the disincentive type,level, and amount based upon how, where, and by how much the risk wasincreased by the disincentivized behavior and/or context. The system maybe configured to: dynamically monitor for and detect the incentivizedbehavior and/or context that lowers the risk of a future occurrence(s)of the behavior(s) by the at least one entity before the behavior(s)occurs; and/or dynamically monitor for and detect the disincentivizedbehavior and/or context that increases the risk of a futureoccurrence(s) of the behavior(s) by the at least one entity before thebehavior(s) occurs.

The system may be configured to: determine whether at least one triggerindicative of a risk of a future occurrence(s) of the behavior(s) by theat least one entity is active or present based on the behavior(s) of theat least one entity and the context(s) associated with the behavior(s)of the at least one entity, as determined through the plurality ofdifferent devices, sensors, sensor arrays, and/or communicationsnetworks and/or inferred through information from system inputs;determine a reward for incentivizing behavior and/or context thateliminates or reduces the at least one trigger; and facilitateredemption of the reward. For example, the system may be configured todetermine whether at least one or more of an Anger trigger, an Anxietytrigger, a Boredom trigger, a Depression trigger, a Fear trigger, and aFrustration trigger that are indicative of a risk of a futureoccurrence(s) of the behavior(s) by the at least one entity is active orpresent based on the behavior(s) of the at least one entity and thecontext(s) associated with the behavior(s) of the at least one entity,as determined through the plurality of different devices, sensors,sensor arrays, and/or communications networks and/or inferred throughinformation from system inputs; determine a reward for incentivizingbehavior and/or context that eliminates or reduces the at least one ormore of an Anger trigger, an Anxiety trigger, a Boredom trigger, aDepression trigger, a Fear trigger, and a Frustration trigger; andfacilitate redemption of the reward.

In an exemplary embodiment, the system may be configured to assess,evaluate, and predict a likelihood of a future occurrence(s) of abehavior(s) and associated context(s) by the at least one entity. Insuch exemplary embodiment, the system may be configured to: dynamicallyand adaptively determine the reward to incentivize behavior and/orcontext that increases the likelihood of a future occurrence(s) of thebehavior(s) by the at least one entity before the behavior(s) occurs;and/or dynamically and adaptively determine the disincentive todisincentivize behavior and/or context that decreases the likelihood ofa future occurrence(s) of the behavior(s) by the at least one entitybefore the behavior(s) occurs. The system may be configured to:dynamically and adaptively determine the reward type, level, and amountbased upon how, where, and by how much the likelihood was increased bythe incentivized behavior and/or context; and/or dynamically andadaptively determine the disincentive type, level, and amount based uponhow, where, and by how much the likelihood was decreased by thedisincentivized behavior and/or context. The system may be configuredto: dynamically monitor for and detect the incentivized behavior and/orcontext that increases the likelihood of a future occurrence(s) of thebehavior(s) by the at least one entity before the behavior(s) occurs;and/or dynamically monitor for and detect the disincentivized behaviorand/or context that decreases the likelihood of a future occurrence(s)of the behavior(s) by the at least one entity before the behavior(s)occurs. The system may be configured to: determine whether at least onetrigger (e.g., an aspiration trigger, a goal trigger, a happinesstrigger, other positive trigger, etc.) indicative of a likelihood of afuture occurrence(s) of the behavior(s) by the at least one entity isactive or present based on the behavior(s) of the at least one entityand the context(s) associated with the behavior(s) of the at least oneentity, as determined through the plurality of different devices,sensors, sensor arrays, and/or communications networks and/or inferredthrough information from system inputs; determine a reward forincentivizing behavior and/or context that produces or increases the atleast one trigger; and facilitate redemption of the reward.

In exemplary embodiments, an AI (Artificial Intelligence) enabled systemis developed, configured, and/or intended to facilitate the offensive,defensive, and logistical capabilities of a person engaged in warfare insome direct form. This encompasses scenarios ranging from the solo unitbehind enemy lines, the operational deployment of forces and resources,to strategic theater. Exemplary embodiments include building orconfiguring a highly advanced and aware system, facilitated by AI,driven by cloud and edge computing, to allow decisions and tacticaladvantage be deployed and calculated at the level it needs first.Exemplary embodiments may be configured to address both the kinetic realworld as well as digital cyberspace, incorporate offensive and defensivetechnology, and be primarily driven by the concept to augment forces tosupport and counter threats as well as malicious enemy AI.

Exemplary embodiments include and/or build upon technology disclosedherein for providing/enabled by AI-driven behavior modification. Forexample, exemplary embodiments disclosed herein adopt a model of:

-   -   1) Context awareness for persons involved, AI-enabled        systems/applications, or both, possibly supplemented with        location elements    -   2) Triggers or events which trigger behavior modification        analysis and suggestions    -   3) Context/Location-based Actions addressing specific triggers        and/or events    -   4) Machine Learning/AI for modifying actions based on new data        and (re)actions related to previous actions    -   5) A Context-dependent computing model—allowing:        -   a. Human Input;        -   b. Mainframes and other centralized computing;        -   c. Standalone and/or peer-to-peer computing;        -   d. Distributed and/or Edge Computing;        -   e. Cloud Resources;        -   f. IoT (Internet of Things) Sensors to all be scaled at an            ad hoc and as available basis; and/or        -   g. a combination/hybrid of the above.

In exemplary embodiments, a system enables both “upstream”/“preemptive”modification (e.g., a Multispectral Camera on a soldier's helmetdetecting ground disturbances that indicate possible minefield or IED(improvised explosive device) placement, etc.) and“downstream”/“reactive” modification (e.g., cameras and acoustic sensorsdetecting incoming mortar fire and guiding forces away from predictedblast areas, etc.).

Exemplary embodiments include systems and methods that enable detectingan immediate threat (formulated as a trigger), but also support“upstream” contexts/behaviors that may lead to a threat(s) or apotential threat(s). In this way, the system can act as both sword andshield, as well as a self-contained “red-team/blue-team” system, that isconstantly probing itself for attacks to increase its capabilitiesacross operational domains Such systems and methods could be used in avariety of ways beyond actual warfare.

For example, the systems and methods can support elements ofgamification. Gamification may include the application of game-designelements and game principles in non-game contexts. Gamification can alsobe defined as a set of activities and processes to solve problems byusing or applying the characteristics of game elements. This can includecausality tracking, financial and resource efficiency, target and uniteffectiveness, and additional items to support the evaluation andimprovement of resources, as well as quantitative and qualitativeelements to encourage continuous improvement, with a mindfulness thatsuch scores could potentially be detrimental to uplifting to morale atvarious levels, times and locations.

For example, with respect to drinking/addiction, a running counter couldcount exactly how many days the person is sober. Coupon awards may thenbe provided or awarded on special milestones like 1 week, 1 month, 100days, a year, etc. With respect to warfare gaming/simulation, suchgaming/scoring mechanisms could track wins and losses, or achievement orprojected achievement of simulated victory (or defeat) goals. This couldinclude augmented reality to make combat training more game-like orlife-like depending on the objective. This could be supported by the useof dashboards or other visualization capabilities to facilitateunderstanding of how and/or how close the objectives of a particularsimulated (or real) “battle” are being achieved or not and why.

The use of grouping/aggregating behaviors is an integral part ofexemplary embodiments of this invention. For example, one or moresuspected “bad” actors may have their behavior/activities monitored fordetection of possible triggers that may indicate upcoming attacks.Through the exemplary sensors, sensor arrays, communications networks,et. al. disclosed herein, the triggers of Fear and/or Excitement may bespecifically monitored to detect “spikes” in or elevated levels aboutthose triggers, possibly driven by an upcoming attack. Similarly, othertriggers such as sudden movements of or discussions about Money may besuch possible attack leading indicator triggers.

The above could be done individually or for multiple persons, orsequentially. For example, if Fear/Excitement is detected in one badactor, the system/method could expand monitoring for those triggers inother possible bad actors, or even in other not-yet-determined-to-be-badpersons whom the bad actor has some sort of contact with. Or,recognizing that an impending attack may have different impact ondifferent people, a different set of sensor monitoring (or other devicemonitoring) may occur, for example, an increase in Anger levels,drinking/substance usage, etc. Or, there could be a change in differentwebsites the person(s) are going to, for example legal sites (e.g., forwill-making purposes, etc.), or in the types of messages that theperson(s) are posting on social media (e.g., “you know I love you,right?”), spikes/new efforts regarding certain kinds of Google searches,a sudden use of anonymous browsers (e.g., Tor) or dark web usage, etc.could be leading indicators, particularly if such usage is tied to knownsuspected triggers (e.g., Fear as a trigger could result in a spike insearches for meditation techniques). And, of course, an increase in anytypes of behavioral interactions between bad actors, for example message“chatter”, could be an indicator of upcoming bad activity, even ifbetween a small number of individuals—even just two.

Changes in physical behaviors/activities and associated triggers can beindicative of upcoming attacks. If individuals are known to be devotedlyreligious for example, a sudden increase in the number/duration ofvisits to a nearby associated religious community center could be aleading indicator of Fear or Stress, and as such indicators of impendingattacks, especially if such increases occur for multiple suspected badactors. As mentioned, a sudden increase in certain “vices” (e.g.,smoking, drinking, etc.) could also be indicators of upcoming attacks asleading indicators regarding Fear, Excitement, Stress, etc.

Changes in overall context for both mental and physical behaviors can beindicators of upcoming bad activity, both individually and plurality ofindividuals/group. For example, a bad actor may suddenly start spendingfar more time at work/working than is typical. Further, if he isperforming activities there (doing personal business for example) thanat home, such change in context for “normal” activities may be cause forsuspicion of upcoming bad activities.

Some/all of the above may be used “progressively” for legal purposes.For example, monitoring of a specific individual for certain triggersmay be authorized (e.g., via warrants, etc.), but not for other possiblebad actors. However, upon detection of certainbehaviors/triggers/associated levels of concern, that, in turn, could berepresented to a judge to authorize broader monitoring of possible badactors for those behaviors/triggers, and/or usage of “traditional”monitoring methods, e.g., phone data, etc.

Overall, aspects of exemplary embodiments disclosed herein enable theability to “visualize” cyber-battles or other kinds of battles, with—orin particular without—the need to actually engage in such battles.

FIG. 16 includes an example diagram of one such potential visualizationof a conflict. Along the leftside in FIG. 16 are spatial dimensions,which may be correlated to context and/or LBS/location-based data. Thesespatial dimensions range from sub-surface, surface level, to variouslevels of airspace, perhaps defined such as the FAA or other suchbreakdown/delineation of control or segmentation. Surface level hasvarious hubs of logistics and command to a squad in the field. Nearsurface may be small UAVs (unmanned aerial vehicles), above thathelicopters or larger drones, at high altitude fighter jets, allsupported and tied together by satellite communications and/or othernetworks. Along the bottom in FIG. 16 is another potential axis of theconflict—ranging from the supporting Civilian economy, to Commandheadquarters, commanding logistical hubs and near conflict rear support.These all aid the Frontline troops. FIG. 16 also illustrates “no-man'sland” of the conflict, where the current battles are taking place,real-time and/or over time. FIG. 16 further illustrates behind enemylines, which will have its own supporting civilian economy, command andcontrol and logistical supply lines. This could be a World War I styletrench warfare context, a 3^(rd) generation warfare context such asWorld War II, or a next generation 4^(th) or 5^(th) generation stylenetwork based warfare system, and/or any further styles or conflict orwarfare that might be developed.

The above breakdown of a conflict type is just one embodiment of aconflict type in this case a traditional war. But other types ofconflict may also apply including but not limited to:

-   -   1. Industrial—AI and safety in the industry, supply chain        optimization, incorporation of IoT response systems, worker        management, etc., all aimed at a conflict of production, e.g.        trade wars (in a much more real sense of the term than is        conventionally or generally used).    -   2. Healthcare—Similar to Industrial conflicts but with added        elements on patient care, outcome and more preemptive emphasis        on safety (such as knowing you need to clean a room or change a        filter), e.g. a conflict with a disease or condition, perhaps        broad-based/infectious. This can be expanded to more longhaul        conditions such a physical therapy, in home assistance (perhaps        insurance subsidized maid service for example is found to reduce        risk and increase health for example), etc. All aimed at a        conflict of saving lives or creating better patient outcomes.    -   3. Scientific—This conflict type categorizes triggers as needs        or opportunities for research, for example, research on        fear/mass panic. It seeks to identify gaps in knowledge via data        management and analysis, it proposes work to be done to help        fill in projected gaps in knowledge or encourage scientists        actually to go and fill in these gaps (e.g., grad student does a        Cryo EM image of a molecule to establish a confirmed        conformation of the molecule in the wild to validate a        hypothetically predicted shape from organic chemistry        calculations, correlated perhaps with known instances of        fear/panic as measured by various sensors et. al.). It would        also emphasize heavily scientific data quality—beginning to        cross-reference scientific conclusions with other studies that        confirm/contradict each other. These get flagged as unresolved        issues.    -   4. Space exploration, colonization, and/or mining (any        location)—AI for safety (safety/conflict avoidance being a        desired goal), as well as placing emphasis on physical layout,        as well as guiding workers. Triggers here could also expand to        material wealth—(e.g., focus on balancing risk and reward to tie        back to the original addiction trigger). It could include        incorporation of more advanced wearables such as space suits,        exoskeleton systems, and embedded biological trackers for        interesting data (e.g., mission control is noticing a drop in        Oxygen levels or detecting trace volatile gasses either down a        mine shaft or on an asteroid).    -   5. Agriculture/Environmental—Incorporating guidance AI into        agricultural systems, emphasis on health and safety, as well as        environmental impact. Also, goal to increase profit while        reducing negative issues, e.g., conflict (in a broad sense)        avoidance.    -   6. Gaming System—Extends AI-type guidance systems to Video Game        or Augmented reality, virtual reality, etc. which might have any        number of goals within the game driven at some level of        conflict.    -   7. Financial Advisor AI—Meant to augment an actual        advisor—trying to protect against particularly bad behaviors        (e.g., panic selling, etc).    -   8. Life Coach, Guardian Angel, aka the Stotic System—Builds on        other systems and methods, and perhaps substantially modifying        them. For example, instead of saying getting away from        addiction, it puts a heavy emphasis on helping someone, in        general, to build a life well-lived, and would perhaps act as a        guardian angel or life coach to help them towards whatever moral        or life goal they want.    -   9. Peacekeeping and conflict resolution—Utilizes peacekeeping in        conflict avoidance, de-escalation, resolution—a key part of        conflict resolution is in fact peacekeeping. A system seeking to        win war must by definition find a way to secure the peace and        the subsequent prosperity required to keep the peace.

Awareness Technology

In exemplary embodiments, the technologies (e.g., FIG. 1, etc.) may beconfigured with the objective that for the “good” system(s) and actor(s)(e.g., persons, applications, algorithms, systems, and/orcombinations/hybrids, etc.) to become aware of and react/respond to“things” (e.g., other “bad” system(s) and/or actors) whose behaviorscould, if left undetected and unchecked, constitute a threat/trigger insome form to the good systems/actors, and to (ideally pre-emptively)formulate action(s) to pre-empt/prevent such threatening behaviors. Thetechnologies may include but are not limited to:

-   -   Various forms of location determination technologies    -   Standalone, peer-to-peer, local, edge, cloud, distributed,        client-server, centralized technologies/systems, and/or        combinations/hybrids    -   Multiple devices, sensors, sensor arrays, and/or communications        networks, including but not limited to IoT, mesh, Zigbee, LPWAN,        star networks. Audio/Optical-related sensors are particularly        important in detecting various physically-threatening behaviors,        as are multi-spectral and hyperspectral imaging and neural        networks    -   Internet(s) of various forms public and private; cloud computing        of various forms    -   Internet of Things (IoT); machine-to-machine (M2M) technologies    -   Trigger detection technologies; trigger-oriented/based support        networks    -   Artificial Intelligence (AR), Virtual Reality (VR), and/or        Augmented Reality (AR) technologies/systems/applications    -   Various form of support networks—physical and/or digital-based    -   Various forms of data collection, processing, and reporting        systems    -   Various forms of predictive analytics systems    -   Various forms of “third parties” for data sourcing, collection,        processing, reporting, and action-related activity        implementation.

Cyberspace Awareness

One issue with effective Cyberspace threat modeling and behaviormodification is that for most people, it represents an ethereal,abstract realm particularly difficult for them to understand what onlinethreats and associated behaviors are occurring or might occur, and howthey can be used against and to various objectives, outcomes, and/orpreferred behaviors and activities.

To this end, exemplary systems and methods disclosed herein will providecontextual awareness at appropriate levels of various domains andtactics. By making these tactics aware to users/“good” actors andsystems, it will help identify and preempt negative consequences, andencourage productive and proactive behavior to further ends. Exemplarysystems and methods disclosed herein will enable the creation, use of,and/or prevention/pre-emption of shadow profiles, false profiles, datavulnerability awareness, location spoofing, security management,predictive AI, data dumps preemption, denial of service, disinformationand scams, programmatically driven attacks, hacks, single-use logins andweak passwords, financial disruption, and quarantining as explainedbelow.

Shadow Profiles—Shadow profiles are “inferred” profiles aboutsomeone/thing (actors) who has not explicitly given their rights to anentity to use the actor(s) data. For example, an actor signs up on asocial media platform and gives that platform access to theircontacts—forms of “supplemental” information above and beyond theactor(s) directly relevant/attributable-to-that-individual data. Theplatform will take that data and put it into a shadow database becauseit knows that people exist with that contact information along withvarious identifiers (e.g., names, phone numbers, etc.). By this kind ofprofile propagation, it becomes possible for the platform to haveknowledge of—e.g., shadow profiles of—far more people/actors than areactually signed up on the platform. Upon detection of potential suchabusive behavior by the social media platform, exemplary systems/methodsdisclosed herein can pre-empt the creation of shadow profiles by variousmeans such as data spoofing, encryption, or other mechanisms to reducethe reliability/accuracy of such supplemental information, providing adeterrent (or at least an obstacle) to the use of/benefits of suchshadow profiles. It is anticipated that these kinds of shadow profilesare already prevalent in existing social media platforms and is being(ab)used by the platforms and by hostile countries.

False Profiles—Another form of cyber warfare (in its broadest sense) isthe creation of false profiles. Whether false attractive people ondating websites, to supposedly sympathetic persons oncommunity-of-interest groups on social media platforms, these falseprofiles can do great damage above and beyond their ability to irritatepeople and overall waste time. They can act as honeypots, or actors forhostile forces. They can be used to recruit and encourage actions thatdestabilize the side. They can be used to siphon off resources from anentity (e.g., getting a victim to send money to address for promises oflove, but that money is used as financing for nefarious purposes). Inexemplary embodiments, one or more of the systems and methods disclosedherein may be incorporated herein and used as a way of vetting/approvingpotential romantic partners and/or to ensure that they/false profilesare not spies of some sort. They can also collect a great deal ofinformation on “real” people—some of it compromising—and potentially usethat information for blackmailing purposes, surveillance and datacollection, and in general thought “guidance” on certain topics, e.g.projection and promotion of certain mental behaviors that (may) runcounter to the interests of the “good” actors. Exemplary systems andmethods disclosed herein will detect/predict the likelihood that a givenprofile is indeed fake/false, and formulate actions to counter suchprofiles, whether it is blocking them, providing (dis)information tothem, and/or otherwise pre-empting the ability of such false profiles tointeract with good actor data and cause/trigger bad actions by goodactors. Exemplary systems and methods disclosed herein would have theability to assist in the creation and formulation of this in addition toits detection.

Data Vulnerability Awareness—This involves the detection and pre-emptionof cyber behaviors that are/may be exploiting consumer data profiles.This can range from specific technical activities (e.g., clicking aknown bad link to a malicious website), to general patterns of behavior(e.g., detecting dangerous internet activity—for example children ofsoldiers engaging in dangerous online activity).

Location Spoofing—While such spoofing exists today (such as in the formof generation of false signals on GPS frequencies), location spoofing isnot limited to direct interference with the location determinationmechanisms—it can instead be focused on spoofing the datacollected/stored/processed/reported. This could includescrambling/encrypting the “true” location data while providing falselocation information “in-the-clear” and/or via other methods known to beused by the “bad” actor(s). A related example would be the generation ofsay 300,000 military-related actors' locations in North Africa, when inreality there are really only 20,000, and they are instead in southernEurope. Another is the ability to obscure/spoof location data that badactors can use to disrupt people, scare people, send abusive messages,and so forth.

Security Management—In addition to/in support of the above, thisincludes enabling use, reuse, and management of various forms of privacyand security mechanisms, including enabling automated attack(s) on badactor data collection systems, such as “slow roll” data generation thatobscures the “time-stamp” of data generation and/or more broadlyprovides defenses that mischaracterizes the existence of, context of,and/or location of good actors, as well as creating new forms ofsecurity from combinations/hybrids of existing privacy/securitymechanisms

Predictive AI—Including detecting/predicting consumer/actor behaviorincluding but not limited to purchasing patterns that can easily beweaponized, and formulating pre-emptive/preventative/mitigatingaction(s) involving Disruptive AI, predicative battlefield AI,False-data-posting-Bots, 50 cent army-type bad actors, and the like.

Data Dumps Preemption—Preemption of the ability to access and “dump” alla good actor's messages. Conversely, such data dumps can be spoofed toprovide false/misleading information, or mixed, e.g. some data are trueand some are not, to bad actors.

Denial of Service—Providing preemptive/preventative/mitigating actionsinvolving denial of service, blocking access, blocking information tobad actors, shadow/false provides, or otherwise undesirable attempts toaccess good actor/system data by bad actors/systems.

Disinformation and scams—Ability to detect/preempt/prevent/mitigate suchnegative behaviors, including the use of “fake news.” Could also be usedin general scams such as falsely claiming to be a grandson in jail tothe person's grandmother, etc.

Programmatically driven attacks—Ability todetect/preempt/prevent/mitigate threats to police, soldiers, etc. (e.g.actors with military/public safety roles), including for example thesending of texts or emails with lists of people and their family photosas direct/indirect threats.

Hacks—Detection/preemption/prevention/mitigation of inperson/programmatic hacks of various forms, including credit reporthacks, and building of shadow profiles.

Single-Use logins and weakpasswords—Detection/preemption/prevention/mitigation of single-uselogins and/or weak passwords.

Financial Disruption—Ability to detect/preempt/prevent/mitigatefinancial negative behaviors, including the use of network disruption,account disruption, destruction of credit/credit scores, etc. Wouldinclude the (ab)use of “social credit” scores and the like, e.g.reputational behavioral attacks on good actors.

Quarantining—Ability to utilize the above in executing/managing ofquarantine operations.

Digital Defenses/Offensive Strategies/Actions

There are a variety of actions enabled/can be used by exemplary systemsand methods of this invention in response to detected (possible)threats/behaviors and/or threats/behaviors in development. Could also beused in offensive capacity. Examples include:

-   -   Internet Disconnect->Using/switching to local resilient systems        that are not dependent on internet, such as:        -   Local or regional internet        -   Edge computing or users given option to disconnect from            primary network for either purposes of stealth, or to combat            against large scale breakdown, infection or infiltration    -   Mesh Networks Usage—Using/switching to self-assembling or        centrally coordinated to support various activities/behaviors of        “good” actors.        -   Units and edge devices, under appropriate conditions, will            interface with other systems on a conditional trust model,            to help each other build out a network of anticipatory            information sharing and provide recommendations to each            other, as well as providing localized cloud support if high            computational power is needed via sharding or similar            computing techniques    -   Different avenues of communication, e.g., backups, land lines,        faxes, etc.—Includes Emergency plans, backup supply lines, etc.        -   Actions will have redundancy and/or alternative, even            rotating device/sensor/network usage, including Bluetooth,            Wi-Fi, ultra-high frequency, laser, Ethernet, satellite,            etc.        -   Strategic use of metadata for where/how information was            sourced and how it was transmitted.    -   Connection Cutting/Safe Mode—Pre-emptive detection of        potentially malicious network behavior causing the network or        node to self-isolate until issue has passed or all clear signals        are sent and verified.        -   Cut or turn off the trans-oceanic underwater cables. Block            satellite traffic.    -   Local/Regional/Countrywide Firewall—Encapsulate/secure parts of        various network geographically, both statically and dynamically.    -   Diversify from major tech providers—Enabling more distributed,        resilient, content and content availability. For example, using        Parler instead of/in addition to Facebook as social media        platform.    -   Parallel Applications/Networks—More broadly, utilizing multiple        apps/networks for the same purpose, for a variety of purposes,        including diversification, backup, and misdirection/spoofing        (sending the correct information on one network, while in        parallel/simultaneously sending incorrect information on another        network, and (possibly) alternating use of such).    -   Data Backups—“known high quality backups”—perhaps leveraging        blockchain and/or “doomsday vault” approaches.    -   Location Spoofing—make location no longer assured, with spoofed        data replacing/intermingled with real/accurate data, as well as        displacement in time e.g., location reported at noon was actual        location at 10:30 am, with algorithm(s) utilized to correct time        stamping once data is received within secure environment. Allow        for the possible creation of disinformation location        networks—basically allow signal skipping between mobile or        location enabled devices to have them not just giving spoofed        signals, but also sending valid signals from invalid locations.    -   Signal Noise—Introduction of noise into data collection and/or        transmission methods to confuse eavesdroppers. Could include        “poor-mans-encryption” that does simple skewing of data such        that it does not seem encrypted but actually is. It could also        include totally false but valid recordings, or “deep fake” type        recordings which could be used to obfuscate or deceive.    -   Shadow Profiles—Spoofing        -   Data Breaches—can use the release of false information to            deliberately create data quality issues and raise costs of            exploitation        -   Multiuse Log in are a single point of failure—Facebook sign            in, google sign in, etc. Incorporation of tools and AI            monitoring to improve the security of various forms of            logins        -   Credit Report Hacks        -   Building of shadow profiles—cross info correlation—phone            number+name+other name (mom, dad, etc.)—correlate to other            addresses, then to credit bureau    -   Data Fuzzing—Introduction of random/unexpected/invalid data to        confuse eavesdroppers.    -   Location Fuzzing—Introduction of random/unexpected/invalid        location data and/or associated metadata to confuse        eavesdroppers.

Kinetic/Physical Domain

Anticipating and pre-empting (if possible, otherwise mitigating)physical threats in the form of underlying/composite behaviors isanother aspect of this invention Similar to patents and patentapplications listed above and incorporated by reference, such asidentification of addiction triggers that may lead to the consumption ofdrugs or alcohol, or partaking in an undesirable activity such asgambling or one that may result in a parole or quarantine violation forpersons-under-restriction, the ability to detect key triggers thatprovide some sort of advance warning and/or predictive basis to believethat an attack may be forthcoming or that an action would create adefensive vulnerability is an important aspect to this invention.

Similar to the threats/behaviors that could occur in the digital world,detection of such triggers/behaviors through sensors, sensor arrays,communications networks enable defensive (re)actions that can pre-emptsuch attacks. Examples include:

-   -   Indicators of Possible/About-to-Occur Incoming Fire Detection—A        variety of behaviors/triggers are possible that can provide        “extra early warning” with respect to possibility of firing.        Examples include sudden troop movements (particularly around        known or suspected gun placements), activation of radar systems,        increased drone/aircraft activity in an area, changes in        electronic warfare patterns, or even changes in satellite        activity. An increase or absence in systemic “behavior” could        also be indicative of a possible threat. For example, it is        known that increased “chatter” on certain kinds of social media        platforms can indicate a higher possibility of a near-term        attack. Such systemic indicators could be across a wide range of        applications and/or networks, ranging from more direct—such as        wargaming-type applications—to indirect, e.g., social media        posting. Other indicators could include increased financial        activity, such as money flows that suddenly jump or change        direction. Even without application-specific activity, a change        in network voice/data traffic (even if content is unknown) could        be a leading indicator of negative behavior.    -   Use of Fast Filters—Edge or other based systems for fast        detection of threats, including increased activity in “upstream”        activities about the development or “implementation” of weapons,        including        -   Minefield, IEDs, etc. construction and placement        -   Establishment of possible Enemy Sniper locations, and/or            increase in number of humans or types of human behavior            in/around previously identified possible such locations    -   Use of Location/Range estimators        -   HUD display of range of various types of weapons, overlaid            on location/regional/country/world maps        -   Increase in purchase activity, permit requests, etc. with            respect to certain kinds of weaponry, particularly if            localized        -   Generally conducting “big data”, AI, and/or predictive            analytics against not just physical movements/activity, but            “mental” activity. For example, historically leaks of            military operations by naïve, well-meaning            personnel—bragging or otherwise—was a major source of            advanced intelligence. Similarly, “grunt” personnel who are            told certain details (even if they don't make sense to the            individual, or by their individual selves don't mean much)            can be observed/aggregated as possible leading indicators of            upcoming negative behaviors.

While the above are discussed in the context of defense, many of themcan be used offensively. For example, indications of “standing-down” byknown or possible bad actor physical gun crews could indicate exhibitingoverconfidence behavior, in turn providing “opportunities” forpre-emptive attacks on those positions. Gaps in minefield/IEDconstruction activity in a given area could provide an avenue for attackby good actors. Similarly, gaps in weapon distribution, defensiveplacements, or certain kinds of personnel could provide opportunitiesfor good actor offensive activity.

Identification of indirect/upstream behavior/triggers can provideopportunities for offense. For example, battle-conducting personnel veryoften have support personnel of at least 2 times. Tracking the behavior,contexts, and/or locations of such personnel can provide importantextra-early warning of upcoming “direct” personnel activity—akin to adrug addict that is susceptible to certain triggers such as Boredom orAnger, and/or exhibits jumps in blood pressure and/or other measurableor observable parameters indicating, for example, Anger, that in turncould lead to drug/alcohol usage. Similarly, while behaviors by “direct”warfare-related personnel are usually highly secured, the behavior ofindirect/support personnel (and systems) are less so. Monitoring keyactivities/behaviors/triggers of those indirect/support personnel andsystems can provide both extra-early warnings (or not) of possiblenegative behavior (e.g., for defensive purposes), but can also be usedfor offensive purposes in identifying gaps/opportunities in bad actordefenses for use in good actor offensive actions.

Such actions could be of all sorts to modify bad actor behavior, again,not just related to traditional “direct” physical weapons. For example,detection of Stress, Powerlessness, and/or Tired triggers (throughvarious sensors/sensor arrays/communications networks) could generateactions specifically intended to exacerbate those triggers/behaviors,such as various propaganda-related activities intended to sap morale andoverall energy/alertness levels. The exemplary systems and methods ofthis invention could detect, through certain behaviors/triggers,opportunities for employing non-convention weapons such as social mediaposts, financial account stress (employing credit card fraud forexample), etc. that could have a far more success in draining bad actorreadiness than direct/traditional methods/weapons might.

Logistical Organization (from Unit to Multiple Organization Levels)

As noted above, the ability to detect/disrupt the behaviors of indirectpersonnel/systems can have a significant role in various forms ofwarfare. This has basis in the oft-cited “an army travels on itsstomach.” In more contemporary terms, the ability to disrupt an ordinarycombatant's digital life can have a similarly outsized impact, e.g., asoldier whose digital life is disrupted is an unhappy, less-effectivesoldier. Such digital disruption, when combined with the physical“stomach-like” impacting disruption, in combination can be far moredisruptive than one element might have individually. To that end, anydigital and/or physical disruptions in a direct/indirect badactor/system can have a major impact, such as through the ability todisrupt:

-   -   Supply Tracking    -   IoT based systems that provide awareness of key resources    -   Distortion of priority needs identification    -   Ability to anticipate actions, making the bad actors much more        reactive    -   Ability to confuse weather and climate monitoring and        anticipation    -   Loss of Supply/Supply Line modeling    -   Disruption of Logistical War Gaming    -   Supply De-Optimization    -   Resilience Modeling disruption (if such and such scenario were        to occur, we'd greatly benefit from a supply depot in XYZ        location)    -   Supply Depot location suggestions, e.g., distort various        conditions to cause misplacement of ground resource        placement/concentration/needs/resources/prioritization    -   Map Modeling (disrupt location-based models of coverage)        -   Heat map of coverage area, reaction time, and time till need            resupply    -   Supply (In)Validation and False Audit—human, robotic and or IoT        based systems to constantly confuse/in-validate that supplies at        location are actually at levels recorded in various databases    -   Quality monitoring and mischaracterization    -   Data de-rectification

Robotic Elements

Of particular note in many of the exemplary embodiments or aspects ofthe invention is the use of robotics, such as unmanned or autonomousvehicles and assets. These are particularly well suited to the captureof behavioral, context, location, and/or trigger information as well asassociated physical elements of persons, equipment, supplies, weapons,and systems. Such robotic elements include but are not limited to:

-   -   Imagery capture—the use of imaging capture element and pattern        recognition to be used to inform the systems are various levels.    -   Edge processing—enabling pushing the necessary computation to        the proximity where it is needed. It is not valuable for a        solider to have its camera data, routed through a unstable        communication network, be processed at a central super computer,        and pushed back out to the results. In a situation where seconds        matter, that kind of delay means death. Instead, the system        would be aware enough to push the optimized calculation near the        edge and reduce latency and bandwidth usage.    -   Defensive Elements (acoustics for monitoring)—The establishment        of sensor networks that are leveraged specifically for defensive        abilities, such as acoustics detecting potential threats.    -   Counter—swarm activities—One of the primary difficulties in next        generation warfare is in the proliferation of cheap, deadly,        disruptive robotics. One of the only effective counters is a        counter-swarm that is deliberately seeking to disrupt and        destroy the threatening robotics. Having this swarm enabled by        AI systems.    -   Threat detection—Use of automated systems to provide threat        detection with a particular focus on pre-empt human detection,        or reaching areas in the world that cannot be readily or cost        effectively be monitored by human assets.    -   Red Team testing—use of robots in a “red team” mode to seek to        probe and train friendly units, e.g., having “hostile” friendly        robots attack a human unit of soldiers, to seek to train and        educate them how to respond to these threats.

Example Embodiments/Scenarios by Location

Below are descriptions of various exemplary embodiments/scenariosinvolving/using various elements disclosed herein. In particular,various resources, actions, and interfaces are described below thatcould be employed in response to various behaviors/triggers.

Tactical Advisement (Individual Unit, could be Solider, Robotic Asset,Etc.)

-   -   a. Fast Filters for Danger—incorporate HUD or other mechanism to        inform user (e.g., voice, haptic, etc.) combined with edge        computing to provide fast filters to identify potential risk and        hazards and seek to augment or at least caution against        potential threats.        -   i. Possible Minefield            -   1. Hyperspectral or multispectral camera—perhaps from a                UAV or perhaps a ground based or exoskeleton based                sensor suite. Detect recent movement in the ground,                perhaps temperature or disturbed ground using AI machine                learning algorithms. This brings up possible issue or                will communicate to team via HUD or voice or other means                to warn of danger.        -   ii. Possible Ambush            -   1. Hyperspectral or multispectral camera—perhaps from a                UAV or perhaps a ground based or exoskeleton based                sensor suite. Detect recent changes in terrain or                unusual shaping patterns using AI machine learning                algorithms. This brings up possible issue or will                communicate to team via HUD Or voice or other means to                warn of danger.    -   b. Course of Action Suggestion        -   i. Target of Opportunity identifier—oh you can take this            hill or accomplish this side goal.        -   ii. Tactical Advantage—Behavior Assessment—Sensors identify            facing battalion X in group Y—Machine Learning suggests that            this group tends to flank left, but only slightly.    -   c. AI Drone Counters—Have your own machine learning profile (you        tend to go left when encountering an obstacle) to empower human        operator to suggest to deliberately go right—the key here is to        cause ‘noise” in the sensors to disrupt machine learning from        finding the pattern    -   d. Acoustic monitoring—backpack mounted sensor suite monitors        person/unit's overall noise level and other factors to create        “visibility score” this score can be passed to units via HUD or        Voice or another device to say hey you're being super noisy etc.        This is useful to counter also other sensor suites.    -   e. IoT Disruption        -   i. Disrupt sensors        -   ii. Create Sensor Static—it is one thing to deny the enemies            sensors, it is perhaps a better thing to deceive.    -   f. Point of Attack on Defenses        -   i. Base Design AI—support teams or constructors—use AI to            assess and evaluate base decisions—this can be done from            both attack and defense—can use algorithms using machine            learning trained off experienced warfighter assessments, or            other mechanisms such as video game modeling.        -   ii. Identify potential hazards, fields of fire, chokepoints,            etc.    -   g. Quarantining        -   i. System detects biological, chemical, radiological or            digital threat            -   1. Guides assets away            -   2. Provides guidance to assets within threat zone on                response to mitigation threat as much as possible        -   ii. Model vectors in and out of affected area, communicates            outward and inward to build consensus of possible issues        -   iii. Adopts a Hazard Avoidance first posture—just because            some elements or actors in system present issue not expected            by other nodes, system should err on the side of assuming            issue is possible threat        -   iv. Calculates impact of false positives and responds to            keep lines effective    -   h. Biometric Monitoring—System enables the monitoring of        soldiers biometrics, such as heart rate, O2 saturation or other        critical elements. These could be predictive to potential        failures in morale in a unit, insight to other factors not        immediately apparent, or other applications.

Operational—This is generally referring to a field of conflict, e.g., abattlefield, a region such as a county, or a state or country, or even atheater.

-   -   a. Logistic Modeling    -   b. Where to enforce    -   c. Possibility of local effect    -   d. Novelty Processing    -   e. Base Construction: Base Design AI—support teams or        constructors—use AI to assess and evaluate base decisions—this        can be done from both attack and defense—can use algorithms        using machine learning trained off experienced warfighter        assessments, or other mechanisms such as video game modeling.

Strategic (Larger Goals Expanding Across Boundaries)

-   -   a. Which zones are more effective at hearts and mind or easy        victories    -   b. Asset placement    -   c. Operational posture        -   i. War Goal Production Management—what to produce

Understanding the Opposition

-   -   (AI meant to deploy or orient beyond friendly lines, into the        enemy)    -   a. Tactical probing    -   b. High Value Target Identification    -   c. Intel identification, including intel of opportunity

No Man's Land—Fog of War

This concept relates to the fog of war, systems and AI will seek topreemptive identify holes in its knowledge and seek to advise theplacement or addition of sensors. These can be simply new placement butcan also be optimizations.

-   -   a. IoT Sensor placement—there is no acoustic sensor within a        general region, the system identifies a hole in its coverage,        perhaps due to a location overlay identifying on a GIS        (Geographic Information System) map there is no coverage in a        local area. System suggests placement of a sensor.    -   b. Sensor Optimization—System identifies that while the sensor        is in place, it is still not covering the area expected—advises        placing sensor higher up on a tower.

AI Optimizations—AI identifies its own weak spots and suggests asbattlefield goals—perhaps expanding on the Sensor optimization, the AIidentifies that while coverage is now better, it still would preferorientation towards the area it identifies as a likely avenue of attack.

Red Team Perspective—Meant to augment and simulate combative posture, toprobe and improve—QA and QC function. System is constantly probingitself. System can shard or be cloned and placed against each otherSimilar to 2 computers playing chess against one another. System can dothis on a digital only or have additional training that have physicalassets.

-   -   Self-Identification failure—preemptive solutions    -   Self-Testing    -   Diagnostic routines to check integrity    -   Specialized data backups to ensure system restoration in event        of disaster recovery    -   Self “knock out” simulation—system identifies that if various        sensor or physical assets are lost, how it would be able to        still maintain some functionality in the mesh.

Media Disruption

Today and tomorrow's warfare utilizes media in various forms to anunprecedented extent. The ability to control, influence, and/or disruptthe media can play a critical role in responding to undesirablebehaviors/triggers by bad actors.

-   -   News Sources—hack or otherwise provide false/misleading        information into news sources known to be “attractive” to bad        actors.    -   News Aggregators—Broader version of news sources, including        various forms of:        -   People,        -   AI,        -   News feeds, etc.    -   News Curators—these are similar to aggregators but do the job of        collecting various information and processing it. These could be        extended to a collection of curators (various “desks” or        niches)—these in turn would be rolled up/consolidated. Could        include:        -   Robotic or AI-assisted curators including an element of            transparency and “confidence” weighting.        -   Human curators who have either public or private allegiances            and a set of motivations to oversee the newsfeed. This could            be to a topic, such as a specific sport, a lifestyle (such            as outdoors), or a broader topics but via curated by a            specific personality or set of personalities.        -   Hybrid model—a combination of human, robotic/AI systems.

As recognized herein, the core of a wargame may be treated like a chessmatch in that the individual maneuvers/tactics of an opponent are not asimportant as it is to decipher how it impacts and/or indicates theopponent's endgame/strategy, so that you can assess as to what degreeyour own strategy will be effective against your opponent's strategy,and, in turn, if/how you should modify your own tactics and overallstrategy. Accordingly, exemplary embodiments disclosed herein areconfigured to determine strategy/intent of an opponent (e.g., one ormore army(ies), a military(ies), an enemy(ies), a potential enemy(ies),a sovereign nation(s) or state(s), and/or a terrorist organization(s),etc.). For example, the tactical reasons why a squad of fighter planesfrom a first country entered the airspace of a second country may not beas important as to understanding the true “upstream” why(s) andassociated motivations for the airspace incursion. Possibilities mightbe to distract from other problems of the first country, to increasedomestic pressure on a leader of the second country to take a moreconciliatory approach to reunification with the first country, to make athird country that supports the second country look weak, to protestupcoming visits by a third country to the second country, etc. If morethan one root cause and/or motivation is present, relative weighting ofthe root causes/motivations may be included.

In exemplary embodiment, a system comprises a plurality of devices,sensors, and communication network(s). The system configured todetermine, through a plurality of measurements/readings taken by theplurality of devices, sensors, and communication network(s), underlyingroot cause(s) and motivation(s) of behavior(s) of at least one asset ofa first group of one or more assets and (a) context(s) associated withthe behavior(s) of the at least one asset; or (b) location and thecontext(s) associated with the behavior(s) of the at least one asset.The system is configured to analyze the measurements/readings, theunderlying root cause(s) and motivation(s) of the behavior(s) of the atleast one asset, and (a) the context(s) associated with the behavior(s)of the at least one asset or (b) the location and the context(s)associated with the behavior(s) of the at least one asset at thelocation, to thereby determine risk(s) associated with the behavior(s)of the at least one asset relative to their impact(s) on behavior(s) andassociated motivation(s) by a second group of one or more assets thatare not part of the first group of one or more assets. The system isconfigured to facilitate one or more actions to lower the risk ofnegative impact(s) of behavior(s) of the first group of one or moreassets on the second group of one or more assets when compared to one ormore threshold(s).

In exemplary embodiments, the system is configured to analyze themeasurements/readings, the underlying root cause(s) and motivation(s) ofthe behavior(s) of the at least one asset, and (a) the context(s)associated with the behavior(s) of the at least one asset or (b) thelocation and the context(s) associated with the behavior(s) of the atleast one asset at the location, to thereby determine warfare strategy,intent, tactic(s), and/or maneuver(s) of the first group of one or moreassets. The system may be configured to select, recommend, and/orimplement one or more actions for the second group of one or more assetsbased on a determination that the implementation of the selected one ormore actions will lower the risk of negative impact(s) of the militarywarfare strategy, intent, tactic(s), and/or maneuver(s) of the firstgroup of one or more assets on the second group of one or more assets.

In exemplary embodiments, the system is configured to determine whetherthe risk of negative impact(s) of behavior(s) of the first group of oneor more assets on the second group of one or more assets is approaching,reached, or exceeded the one or more threshold(s). The system isconfigured to select, recommend, and/or implement one or more actionsfor the second group of one or more assets based on a determination thatthe implementation of the selected one or more actions will lower therisk of negative impact(s) of behavior(s) of the first group of one ormore assets on the second group of one or more assets to below the oneor more threshold(s).

In exemplary embodiments, the system is configured to analyze themeasurements/readings, the underlying root cause(s) and motivation(s) ofthe behavior(s) of the at least one asset, and (a) the context(s)associated with the behavior(s) of the at least one asset or (b) thelocation and the context(s) associated with the behavior(s) of the atleast one asset at the location, to thereby determine an origination ofthe at least one asset, which analysis including the origination of theat least one asset is usable by the system in determining whether the atleast one asset should be considered a threat.

In exemplary embodiments, the at least one asset of the first group ofone or more assets comprises at least one piece of data. The system isconfigured to determine an origination of the at least one piece ofdata, which determination is usable by the system in determining whetherthe at least one piece of data should be considered a virtual threat.

In exemplary embodiments, the underlying root cause(s) and motivation(s)of behavior(s) of the at least one asset comprise one or moremotivation(s) associated with one or more upstream behavior(s) beforethe occurrence of the behavior(s) of the at least one asset that aredirectly or indirectly cause the behavior(s) of the at least one assetand that triggered one or more events, actions, and/or activities thatresulted in the behavior(s) of the at least one asset.

In exemplary embodiments, the system is configured to use a proofblockchain system or other distributed ledger technology consensusmechanism for privacy protection and security.

In exemplary embodiments, the system is configured to use a blockchainsystem or other distributed ledger technology for data anonymization.

In exemplary embodiments, the system is configured to use one or moreblockchain components including at least one of a distributed network, awallet, and/or a user profile; and/or the system is configured to useartificial intelligence to observe and examine a blockchain trail tomake predictions identifying behaviors and/or behavior patterns.

In exemplary embodiments, the system is configured to use a proofblockchain system or other distributed ledger technology consensusmechanism for storing data collected by the plurality of devices,sensors, and communication network(s), the determinations made by thesystem, the one or more threshold(s), and/or the one or more actions tolower the risk of negative impact(s) of behavior(s) of the first groupof one or more assets on the second group of one or more assets.

In exemplary embodiments, the system is configured to be operable fordetermining the underlying root cause(s) and motivation(s) ofbehavior(s) of the at least one asset of the first group of one or moreassets by monitoring and assessing performance and underlyingmotivation(s) associated with behavior(s) and associated event(s),activity(ies), and context(s) collected via a distributed geographicalcomputing and/or network environment(s).

In exemplary embodiments, the system is configured to be operable fordetermining the underlying root cause(s) and motivation(s) ofbehavior(s) of the at least one asset of the first group of one or moreassets by: storing a plurality of existing and/or historical events,activities, and context records, wherein each existing and/or historicalevent, activity, and context record describes a corresponding event,activity, and context in a distributed computing and/or networkenvironment, wherein a previous baseline of motivations associated withevents, activities, and contexts has been established and is stored in abaseline motivational profile for various groups inclusive of a group(s)being analyzed; receiving, by a motivational analysis engine, anincoming event, activity, and/or context record describing acorresponding event, activity, and/or context in the distributedcomputing and/or network environment; performing an assessment ofpotential motivation(s) underlying the received event, activity, and/orcontext record by comparison to a previously encountered similarevent(s), activity(ies) and/or context(s); determining a degree to whichthe received event, activity, and/or context record is consistent withthe baseline motivational profile for the similar event(s),activity(ies) and/or context(s), and to the extent there are anydifferences exceeding a certain threshold, assessing, calculating, anddeveloping a statistical profile and associated probabilities of thedifferences being caused by motivations not in the baseline motivationalprofile or in material modifications to historically demonstratedmotivations; assessing the risk(s) of new/modified motivations for theevent, activity, and/or context being applied to other event(s),activities, and/or contexts; assessing a second group(s) ofmotivation(s) relative to a first group(s) of behavior(s) relative tothe current and historical event(s), activities, and/or contexts;developing potential actions for the second group(s) that could negateor mitigate the negative effects of the new/modified motivations for theevent, activity, and/or context of the first group(s); and developingthe potential actions for an existing baseline motivational profile ofthe second group(s) as well as potential actions if the baselinemotivational profile of the second group(s) is changed, the potentialactions including one or more tasks to be performed, resources required,skills required, human, physical, and virtual assets required,additional data monitoring and associated capabilities required.

In exemplary embodiments, the least one asset of the first group of oneor more assets comprises at least one person of a first group of one ormore persons; and the second group of one or more assets comprise asecond group of one or more persons that are not part of the first groupof one or more persons; and/or the least one asset of the first group ofone or more assets comprises at least one object of a first group of oneor more objects; and the second group of one or more assets comprise asecond group of one or more objects that are not part of the first groupof one or more objects.

In exemplary embodiments, the least one asset of the first group of oneor more assets comprises at least one object of a first group of one ormore objects. The second group of one or more assets comprise a secondgroup of one or more objects that are not part of the first group of oneor more objects. The objects comprise one or more of military equipment,armaments, and/or personnel.

In exemplary embodiments, the assets of the first and second groups ofone or more assets comprise one or more human assets, physical assets,and/or a virtual assets.

In exemplary embodiments, the first group of one or more assets compriseone or more of an army(ies), a military(ies), an enemy(ies), a potentialenemy(ies), a sovereign nation(s) or state(s), and/or a terroristorganization(s). The second group of one or more assets comprise one ormore of an army(ies), a military(ies), an enemy(ies), a potentialenemy(ies), a sovereign nation(s) or state(s), and/or a terroristorganization(s) that are not part of the first group of one or moreassets.

In exemplary embodiments, the system is configured to allow additions,deletions, and modifications to the one or more thresholds and to theplurality of devices, sensors, and communications network(s) includingmodifying one or more of the settings, increasing or decreasing afrequency of data collection, modifying how data is collected, andmodifying type of data collected.

In exemplary embodiments, the system is configured to: receive andprocess feedback including the context(s) and/or assessment of therisk(s) associated with the behavior(s) of the at least one assetrelative to their impact(s) on behavior(s) and associated motivation(s)by a second group of one or more assets that are not part of the firstgroup of one or more assets. In response to the feedback, the system isconfigured to adjust the plurality of devices, sensors, andcommunications network(s) including modifying one or more of thesettings, increasing or decreasing a frequency of data collection,modifying how data is collected, and modifying type of data collected.

In exemplary embodiments, the at least one asset comprises at least oneperson. The system is configured to determine the risk(s) associatedwith the behavior(s) of the at least one person relative to theirimpact(s) on behavior(s) and associated motivation(s) by the secondgroup of one or more assets relative to the one or more thresholdsthrough analysis of information related to the underlying root cause(s)and motivation(s) of behavior(s) of the at least one person includingthe plurality of measurements/readings taken by the plurality ofdevices, sensors, and communications network(s), and (a) the context(s)associated with the behavior(s) of the at least one person; or (b) thelocation and the context(s) associated with the behavior(s) of the atleast one person. The information related to the underlying rootcause(s) and motivation(s) of behavior(s) of the at least one personincludes one or more of personal physical data, body state data, mentalstate related data, environmental data, and activity data.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes at least three or more 5W1H (who,what, when, where, why, how) attributes.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes at least one environmental condition.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes where is the location of the at leastone asset, when the at least one asset is at the location, and anenvironmental condition at the location.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset comprises one or more of a situation, anenvironmental condition, and a state of mind of the at least one asset.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset comprises: a situation and an environmentalcondition; an environmental condition and a state of mind of the atleast one asset; a situation and a state of mind of the at least oneasset; or a situation, an environmental condition, and a state of mindof the at least one asset.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes at least three or more of: where isthe location of the at least one asset; why the at least one asset is atthe location; who (if anyone) is virtually and/or actually with the atleast one asset at the location or nearby the location within audible,visual, and/or electronic detection range of the devices, sensors,and/or communication network(s); what the at least one asset is doing atthe location; when the at least one asset is at the location; how the atleast one asset arrived at the location and/or how will the at least oneasset leave the location; and an environmental condition at thelocation.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes at least three or more of: where isthe at least one asset; why is the at least one asset exhibiting thebehavior(s); who (if anyone) is virtually and/or actually with the atleast one asset or nearby the at least one asset within audible, visual,and/or electronic detection range of the devices, sensors, and/orcommunication network(s); what is the at least one asset doing; when isthe at least one asset exhibiting the behavior(s); how is the at leastone asset exhibiting the behavior(s); and an environmental condition.

In exemplary embodiments, the plurality of devices, sensors, andcommunication network(s) comprises at least two different types ofdevices, sensors, and communication network(s) configured to take aplurality of different types of measurements/readings.

In exemplary embodiments, the system is configured to determine thecontext(s) associated with the behavior(s) of the at least one assetincluding why the at least one asset is exhibiting the behavior(s) basedon at least one or more of: where is the at least one asset; who (ifanyone) is virtually and/or actually with the at least one asset ornearby the at least one asset within audible, visual, and/or electronicdetection range of the devices, sensors, and/or communicationnetwork(s); what is the at least one asset doing; when is the at leastone asset exhibiting the behavior(s); how is the at least one assetexhibiting the behavior(s); and at least one environmental condition.

In exemplary embodiments, the system is configured to monitor result(s)of the one or more actions to thereby determine effectiveness of risklowering action(s) and determine modification(s), if any, to profileparameter(s), risk determination algorithm(s), and/ormeasurements/readings taken by the plurality of devices, sensors, andcommunications network(s).

In exemplary embodiments, the at least one asset comprises at least onehuman asset, and the behavior(s) of at least one human asset comprises ahuman behavior; and/or the at least one asset comprises at least onephysical asset, and the behavior(s) of at least one physical assetcomprises a physical activity including movement or relocation of thephysical asset; and/or the at least one asset comprises at least onevirtual asset, and the behavior(s) of at least one virtual assetcomprises a virtual activity.

In exemplary embodiments, a system comprises a plurality of devices,sensors, and communication network(s). The system is configured todetermine, through a plurality of measurements/readings taken by theplurality of devices, sensors, and communication network(s), behavior(s)of at least one asset and (a) context(s) associated with the behavior(s)of the at least one asset; or (b) location and the context(s) associatedwith the behavior(s) of the at least one asset. The context(s)associated with the behavior(s) of the at least one asset includes atleast three or more 5W1H (who, what, when, where, why, how) attributes;and/or the system is configured to determine the context(s) associatedwith the behavior(s) of the at least one asset including why the atleast one asset is exhibiting the behavior(s) based on at least one ormore of: where is the at least one asset; who (if anyone) is virtuallyand/or actually with the at least one asset or nearby the at least oneasset within audible, visual, and/or electronic detection range of thedevices, sensors, and/or communication network(s); what is the at leastone asset doing; when is the at least one asset exhibiting thebehavior(s); how is the at least one asset exhibiting the behavior(s);and at least one environmental condition.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes at least three or more of: where isthe at least one asset; why is the at least one asset exhibiting thebehavior(s); who (if anyone) is virtually and/or actually with the atleast one asset or nearby the at least one asset within audible, visual,and/or electronic detection range of the devices, sensors, and/orcommunication network(s); what is the at least one asset doing; when isthe at least one asset exhibiting the behavior(s); how is the at leastone asset exhibiting the behavior(s); and an environmental condition.

In exemplary embodiments, the context(s) associated with the behavior(s)of the at least one asset includes: where is the location of the atleast one asset; when the at least one asset is at the location; and anenvironmental condition at the location.

In exemplary embodiments, the system is configured to determine, throughthe plurality of measurements/readings taken by the plurality ofdevices, underlying root cause(s) and motivation(s) of the behavior(s)of the at least one asset.

In exemplary embodiments, the system is configured to analyze themeasurements/readings, the underlying root cause(s) and motivation(s) ofthe behavior(s) of the at least one asset, and (a) the context(s)associated with the behavior(s) of the at least one asset or (b) thelocation and the context(s) associated with the behavior(s) of the atleast one asset at the location, to thereby determine risk(s) associatedwith the behavior(s) of the at least one asset relative to impact(s) onbehavior(s) and associated motivation(s) by at least one other asset. Inexemplary embodiments, the system is configured to facilitate one ormore actions to lower the risk of negative impact(s) of the behavior(s)of the at least one asset on the at least one other asset when comparedto one or more threshold(s). In exemplary embodiments, the system isconfigured to monitor result(s) of the one or more actions to therebydetermine effectiveness of risk lowering action(s) and determinemodification(s), if any, to profile parameter(s), risk determinationalgorithm(s), and/or measurements/readings taken by the plurality ofdevices, sensors, and communications network(s).

In exemplary embodiments, the system is configured to analyze themeasurements/readings, the behavior(s) of the at least one asset, and(a) the context(s) associated with the behavior(s) of the at least oneasset or (b) the location and the context(s) associated with thebehavior(s) of the at least one asset at the location, to therebydetermine a risk of a second behavior(s) of the at least one assetrelative to a trigger threshold(s). In exemplary embodiments, the systemis configured to facilitate one or more actions to lower the risk of thesecond behavior(s) associated with a trigger(s) from reaching orexceeding the trigger threshold(s) associated with the secondbehavior(s) before the second behavior occurs. In exemplary embodiments,the system is configured to monitor result(s) of the one or more actionsto thereby determine effectiveness of risk lowering action(s) anddetermine modification(s), if any, to profile parameter(s) of the atleast one asset, risk determination algorithm(s) and/ormeasurements/readings taken by the plurality of devices, sensors, andcommunications network(s) relative to the trigger(s) and/or the secondbehavior(s) of the at least one asset.

In exemplary embodiments, the at least one asset comprises one or moreof a human asset, a physical asset, and/or a virtual asset.

Also disclosed are exemplary computer-implemented methods for monitoringand assessing performance and underlying motivation(s) associated withbehaviors and associated events, activities, and contexts collected viaa distributed geographical computing, and/or network environment(s). Inexemplary embodiments, a method comprises: storing a plurality ofexisting and/or historical events, activities, and context records,wherein each existing and/or historical event, activity, and contextrecord describes a corresponding event, activity, and context in adistributed computing and/or network environment, wherein a previousbaseline of motivations associated with events, activities, and contextshas been established and is stored in a baseline motivational profilefor various groups inclusive of a group(s) being analyzed; receiving, bya motivational analysis engine, an incoming event, activity, and/orcontext record describing a corresponding event, activity, and/orcontext in the distributed computing and/or network environment;performing an assessment of potential motivation(s) underlying thereceived event, activity, and/or context record by comparison to apreviously encountered similar event(s), activity(ies) and/orcontext(s); determining a degree to which the received event, activity,and/or context record is consistent with the baseline motivationalprofile for the similar event(s), activity(ies) and/or context(s), andto the extent there are any differences exceeding a certain threshold,assessing, calculating, and developing a statistical profile andassociated probabilities of the differences being caused by motivationsnot in the baseline motivational profile or in material modifications tohistorically demonstrated motivations; assessing the risk(s) ofnew/modified motivations for the event, activity, and/or context beingapplied to other event(s), activities, and/or contexts; assessing asecond group(s) of motivation(s) relative to a first group(s) ofbehavior(s) relative to the current and historical event(s), activities,and/or contexts; developing potential actions for the second group(s)that could negate or mitigate the negative effects of the new/modifiedmotivations for the event, activity, and/or context of the firstgroup(s); and developing the potential actions for an existing baselinemotivational profile of the second group(s) as well as potential actionsif the baseline motivational profile of the second group(s) is changed,the potential actions including one or more tasks to be performed,resources required, skills required, human, physical, and virtual assetsrequired, additional data monitoring and associated capabilitiesrequired.

In exemplary embodiments, the computer-implemented method may include orrely upon blockchain technology or other distributed ledger technologyconsensus mechanism for privacy protection and security. For example,data elements that are collected may be stored using a blockchain systemor other distributed ledger technology.

In exemplary embodiments, the computer-implemented method includesassessing, via the moral and/or motivational analysis engine, inputsinto and/or outputs from the system including evaluation of one or moreincoming requests, external requests, actions from the system, andactions from others, which evaluation may include potential tasking andassessment of overall morality of actions and/or ethics of actions. Therequests may be any type of request. In the case of a research engine,the moral and/or motivational analysis engine may include evaluationwhether the research question or requirements were moral—for example,research into pain thresholds may provide some value, but overtinfliction of pain is seen as immoral. The moral and/or motivationalanalysis engine would need to balance the needs of knowledge versus theethics of acquiring that knowledge. Other examples would be requests ofinformation coming out of the system as tasking, to ensure that it is anethical request. In a military context, action could include potentiallyharming civilians, which is generally seen as unethical. In this latterexample, the moral and/or motivational analysis engine could vetosuggested unethical action. The moral and/or motivational analysisengine may also be configured to weigh and/or balance a need or desiredoutcome versus consequence(s) or collateral damage, e.g., in order todetermine what is willing to be done and under whatcircumstances/context, etc. For example, the moral and/or motivationalanalysis engine may weigh and/or balance a need of capturing a highprofile target (e.g., terrorist, etc.) versus possible collateraldamage, e.g., possible and/or types of civilian casualties (e.g., lessthan 10 civilian casualties, no children casualties, etc.), at asecluded location with a low probability of civilian casualties, etc. Inthis example, the strategic imperative of not harming civilians may bewaived or parameter(s) may be adjusted such that civilian harm is deemedallowable at a certain minimum level in the case of capturing a veryhigh profile target or under other situation(s).

In exemplary embodiments, the computer-implemented method includesallowing user intervention to prevent and/or address prohibited,wrongful, immoral, and/or unethical action(s) of the system andproviding machine learning to inhibit the system from repeat performanceof the prohibited, wrongful, immoral, and/or unethical action(s).Examples of prohibited actions could be war crimes, attacks againstcivilians, severe environmental damage (e.g., salting the earth, usingnuclear weapons to make areas uninhabitable, etc.). Additional examplescould be pain research, other research that could be experiments thatresult in damage to life or liberty, Nuremberg accords violations, etc.An assortment of various controls may be implemented in exemplaryembodiments, such as intervention by qualified and credentialedpersonnel, use of “therapy” to restore viability—to refresh the moralengine and try and remove misconfigured ethics, simply restoring to aprevious version, etc.

In exemplary embodiments, the computer-implemented method includesdetermining, via a reputation engine connected with the system, whethera user is untrustworthy based one or more wrongful, disreputable,immoral, and/or unethical user actions and/or requests by the user. Andif the user is determined to be untrustworthy, the computer-implementedmethod includes reducing user privilege(s) to the system and/or lockingthe user out from system. By way of example, a wrongful action could berepeated attempts to try and coax the system into bad actions. Anexample of this would be a nefarious actor trying to get the system tohurt civilians. In this example, the system could correlate the“bad/evil” requests to the requestor/actor, and see a pattern that thisperson is repeatedly trying to make the system do “bad” things. Inresponse, the the system could then flag the actor as a “bad” actor, andreduce their user privileges and/or lock the user out entirely from thesystem.

Exemplary embodiments are disclosed of AI/human systems for theevaluation, quality control, remediation, direction, and instruction ofdata. In exemplary embodiments, a system or series of systems in variousforms of hierarchy categorizes data driven needs and uses theseanomalies in the data to drive triggers to rectify the situation, suchas needs or opportunities for research. The system(s) seeks to identifygaps in knowledge via data management and analysis. The system(s)proposes work to be done to help fill in projected gaps in knowledge orencourage scientists actually to go and fill in these gaps (e.g., gradstudent go do a Cryo EM image of this molecule to establish a confirmedconformation of the molecule in the wild to validate a hypotheticallypredicted shape from organic chemistry calculations, etc.). Thesystem(s) would emphasize heavily scientific data quality—beginning tocross-reference scientific conclusions with other studies thatconfirm/contradict each other. These get flagged as unresolved issues.The system(s) would incorporate elements of reputation and rewards toincentivize the work, be scalable to various types of data, allow forthe incorporation of ethical or moral frameworks around the data (suchas but not limited to animal testing). The system(s) would be inherentlyextensible as the system(s) seeks to expand more data sources.Additionally, the system(s) could serve as an information systeminstructional system that guides students to fill or validate variousplaces in the data and trains the students into more autonomousresearchers that would, in turn, grow to guide the system(s).

Types of Datasets

-   -   Team data—one research team    -   Academic—internal research within a university for example,        across multiple departments    -   Public knowledge (e.g., Wikipedia)    -   Internal knowledge—corporate data lake, various data bases    -   Asset and Inventory management    -   System state validation—validation that if asset information        repository states system state is expected to have xyz values,        that the asset actually has those values, or directs a        validation and/or remediation effort to put it in the proper        state and update the data to correct values.    -   Scientific knowledge (data in journals and other sources)—for        example well known ‘reproducibility crisis’ the system(s) would        allow for a rapid formulation of what scientific suppositions        have not been reproduced, or have seen contradictory efforts.        This could be applied to a confidence score or other value        (numeric or qualitative) on the accuracy and validity of the        finding    -   Any combination of one or any of these    -   Apply to internal, external, or holistic view of data—extensible        by design

Functions of an AI System

This can direct new activities to fill in a gap, to validate existing orcontradictory elements in a body of knowledge, remediation or resolveissues in missing, incomplete, contradictory, or inaccurate data, or toinstruct others in the accumulation of their own knowledge (such ashelping guide a graduate student towards useful research topics). Eachof these data concerns, can be classified as triggers with a resultingand associated behavioral response(s) that can be applied to addressthem in various ways.

Trigger Examples Example Potential Responses Missing Data 1) Generate atask ticket to investigate and fill data 2) Execute a data remediationtask to find out if this data should be present and why it is not 3)Restore database connectivity and validate missing data restored StaleData 1) Generate ticket to conduct revised experiment to update data 2)Root cause analysis 3) Find alternative data source and remediate andrealign data (with caveats) Contradictory data 1) Flag item ascontradictory evidence 2) Generate multiple task tickets (such as adouble blind) to resolve issue Incomplete data Generate bounty to seeklab team to complete the picture Inaccurate data Flag data as notmatching expected results, flag for further investigation - this couldbe unreliable data, or the model for example could have been bad and themodel itself needs improvements. Non-reproducible Data Similar tocontradictory, however, the data has been found to not be the case innumerous experiments. Flag the data as such, not removing the data butaffecting its confidence score Data lacks confirmed labels andcategorization Generate ticket to validate via random parties to read,label, and cross reference correct classification parameters.

FIG. 17 illustrates an example system according to an exemplaryembodiment focused on medicine and drug discovery for treatment ofdisease, including need-based driving, missing data collection, growingextensibility, skill instructional framework, human and AI elements,reputation management with the world, a moral compass/ethical frameworkto guide, and additional tasks. FIG. 18 illustrates an example systemaccording to an exemplary embodiment including a moral compass.

Driving Research and Development or General Discovery Through Direction

The exemplary system(s) points out areas quickly that need more work,and auto generates the tickets via a simple cross reference. One commonissue for new researchers, such as at the graduate level in universitysystems, is the need to do thorough literature reviews to discover whatit is that needs discovering. The system could help to rapidly assist inthis process, and the students work in researching will help the systemsto categories and label various papers, or confirm that the labels onthe papers are correct (for example, a paper is labeled having to dowith economics, but is in fact primarily a behavioral psychology paperwith economic implications).

Rewards and Reputation Systems Incentivizing

The exemplary system(s) could generate rewards, such as cryptocurrencytokens, reputation points, cash bounties, grant access or other items toincentivize participation in the data gathering and processing.

The exemplary system(s) may also include incorporation of moralframework, need based—behavioral modification, and risk Assessment suchas hazard scoring, confidence scoring, moral responsibility frameworkand mitigation recommendations.

Extensible Design

The exemplary system(s) is by design extendable—different types ofdatabases, different domains of knowledge, with established ability toextend and incorporate (such as a disease focused AI integrating with apublic health focused AI).

Human Driven Requests

Humans can drive requests and open tickets. The exemplary system(s) maynot really comprise “one” thing, but rather could be a single AI, a teamplus some level of automation, or perhaps more a hive mind approachsimilar to things like Wikipedia.

Instruction as Experimental Opportunity:

The exemplary system(s) could seek to refresh scientific data, forquality control purposes, so the system(s) may guide graduate studies toperiodically ‘revaluate’ various scientific data points. These can bedriven by AI guidance or things like neural networks which have foundthat various experiments produce a higher quality scientist or engineer,for example. This may include instructing agents in simple experimentsof dropping a feather and a bowling ball in a vacuum to observe thatgravity is affecting both the feather and the bowling ball at the samerate.

This exemplary instruction system(s) could be used to augment, or evenreplace elements of traditional systems, developing full courses invarious fields, such as an aspiring engineer or doctor being trained inbiology, chemistry, physics, etc., building into larger areas of studysuch as mechanical engineering or oncology. This experience can beincorporated into a reputation score as well as a resume or CV type“character sheet” similar to video games, along with associated tasksthat were conducted to earn those reputation or skill points, addingcredibility. From there, the instruction system(s) would not simply beguiding students in meaningful research but building them into qualifiedresearchers that would move from simply responding to the system(s), butrather helping guide towards novel and interesting research areas.

Exemplary embodiments may include one or more computing devices, such asone or more servers, workstations, personal computers, laptops, tablets,smartphones, person digital assistants (PDAs), etc. In addition, thecomputing device may include a single computing device, or it mayinclude multiple computing devices located in close proximity ordistributed over a geographic region, so long as the computing devicesare specifically configured to function as described herein. Further,different components and/or arrangements of components than illustratedherein may be used in the computing device and/or in other computingdevice embodiments.

Exemplary embodiments may include one or more processors and memorycoupled to (and in communication with) the one or more processors. Aprocessor may include one or more processing units (e.g., in amulti-core configuration, etc.) such as, and without limitation, acentral processing unit (CPU), a microcontroller, a reduced instructionset computer (RISC) processor, an application specific integratedcircuit (ASIC), a programmable logic device (PLD), a gate array, and/orany other circuit or processor capable of the functions describedherein.

In exemplary embodiments, a memory may be one or more devices thatpermit data, instructions, etc., to be stored therein and retrievedtherefrom. The memory may include one or more computer-readable storagemedia, such as, without limitation, dynamic random-access memory (DRAM),static random-access memory (SRAM), read only memory (ROM), erasableprogrammable read only memory (EPROM), solid state devices, flashdrives, CD-ROMs, thumb drives, and/or any other type of volatile ornonvolatile physical or tangible computer-readable media.

In exemplary embodiments, computer-executable instructions may be storedin the memory for execution by a processor to particularly cause theprocessor to perform one or more of the functions described herein, suchthat the memory is a physical, tangible, and non-transitory computerreadable storage media. Such instructions often improve the efficienciesand/or performance of the processor that is performing one or more ofthe various operations herein. It should be appreciated that the memorymay include a variety of different memories, each implemented in one ormore of the functions or processes described herein.

In exemplary embodiments, a network interface may be coupled to (and incommunication with) the processor and the memory. The network interfacemay include, without limitation, a wired network adapter, a wirelessnetwork adapter, a mobile network adapter, or other device capable ofcommunicating to one or more different networks. In some exemplaryembodiments, one or more network interfaces may be incorporated into orwith the processor.

It should be appreciated that the functions described herein, in someembodiments, may be described in computer executable instructions storedon a computer readable media, and executable by one or more processors.The computer readable media is a non-transitory computer readablestorage medium. By way of example, and not limitation, suchcomputer-readable media can include RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that can be used to carry or store desiredprogram code in the form of instructions or databases and that can beaccessed by a computer. Combinations of the above should also beincluded within the scope of computer-readable media.

It should also be appreciated that one or more aspects of the presentdisclosure transform a general-purpose computing device into aspecial-purpose computing device when configured to perform thefunctions, methods, and/or processes described herein.

Example embodiments are provided so that the present disclosure will bethorough and will fully convey the scope to those who are skilled in theart. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms, and that neither should be construed to limit the scope of thepresent disclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail. For example, technical material that is known inthe technical fields related to the present disclosure has not beendescribed in detail so that the present disclosure is not unnecessarilyobscured. This includes, but is not limited, to technology utilized indetermining the location of mobile devices via a variety of means. Inaddition, advantages and improvements that may be achieved with one ormore exemplary embodiments of the present disclosure are provided forpurposes of illustration only and do not limit the scope of the presentdisclosure, as exemplary embodiments disclosed herein may provide all ornone of the above-mentioned advantages and improvements and still fallwithin the scope of the present disclosure.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. In addition, as used herein, the term “or” is an inclusive“or” operator and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The terms “comprises,” “comprising,”“including,” and “having,” are inclusive and therefore specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. The method steps, processes, andoperations described herein are not to be construed as necessarilyrequiring their performance in the particular order discussed orillustrated, unless specifically identified as an order of performance.It is also to be understood that additional or alternative steps may beemployed.

The term “based on” is not exclusive and allows for being based onadditional factors not described, unless the context clearly dictatesotherwise. The term “network” is used in multiple contexts within thepresent disclosure, and its use generally (but not necessarily) fallsinto one of two categories. The first is in the form of a (generallyhuman) support “network” including one or more individuals/entities thatprovide the addict or other person with some sort of support orassistance. The second is in a technical context, such as acommunications network that transmits, receives, and/or otherwiseprovides technical connectivity between various technical componentsdisclosed herein.

As used herein, the terms “support network” and “support community”refer to a concept that an individual's or groups of individuals'personal network of friends, family colleagues, coworkers,medical/mental health/addiction professionals, members of their socialnetwork (e.g., Facebook, Twitter, Snapchat, etc.), etc. and thesubsequent connections within those networks can be utilized to findmore relevant connections for a variety of activities, including, butnot limited to dating, job networking, service referrals, contentsharing, like-minded individuals, activity partners, or the like. Suchsocial network may be created based on a variety of criteria, including,for example, an address book, a social event, an online community, orthe like. As used herein, the term “member” refers to a user who isincluded in a support network. The term “group” or “community” refers toa collection of members.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, or features, these elements,components, or features should not be limited by these terms. Theseterms may be only used to distinguish one element, component, or featurefrom another element, component, or feature. Terms such as “first,”“second,” and other numerical terms when used herein do not imply asequence or order unless clearly indicated by the context. Thus, a firstelement, component, or feature could be termed a second element,component, or feature without departing from the teachings of theexample embodiments.

None of the elements recited in the claims are intended to be ameans-plus-function element within the meaning of 35 U.S.C. § 112(f)unless an element is expressly recited using the phrase “means for,” orin the case of a method claim using the phrases “operation for” or “stepfor.”

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure. Individual elements,intended or stated uses, or features of a particular embodiment aregenerally not limited to that particular embodiment, but, whereapplicable, are interchangeable and can be used in a selectedembodiment, even if not specifically shown or described. The same mayalso be varied in many ways. Such variations are not to be regarded as adeparture from the present disclosure, and all such modifications areintended to be included within the scope of the present disclosure.

What is claimed is:
 1. A system comprising a plurality of devices,sensors, and communication network(s), the system configured determine,through a plurality of measurements/readings taken by the plurality ofdevices, sensors, and communication network(s), underlying root cause(s)and motivation(s) of behavior(s) of at least one asset of a first groupof one or more assets and (a) context(s) associated with the behavior(s)of the at least one asset; or (b) location and the context(s) associatedwith the behavior(s) of the at least one asset; analyze themeasurements/readings, the underlying root cause(s) and motivation(s) ofthe behavior(s) of the at least one asset, and (a) the context(s)associated with the behavior(s) of the at least one asset or (b) thelocation and the context(s) associated with the behavior(s) of the atleast one asset at the location, to thereby determine risk(s) associatedwith the behavior(s) of the at least one asset relative to theirimpact(s) on behavior(s) and associated motivation(s) by a second groupof one or more assets that are not part of the first group of one ormore assets; and facilitate one or more actions to lower the risk ofnegative impact(s) of behavior(s) of the first group of one or moreassets on the second group of one or more assets when compared to one ormore threshold(s).
 2. The system of claim 1, wherein the system isconfigured to analyze the measurements/readings, the underlying rootcause(s) and motivation(s) of the behavior(s) of the at least one asset,and (a) the context(s) associated with the behavior(s) of the at leastone asset or (b) the location and the context(s) associated with thebehavior(s) of the at least one asset at the location, to therebydetermine warfare strategy, intent, tactic(s), and/or maneuver(s) of thefirst group of one or more assets.
 3. The system of claim 2, wherein thesystem is configured to select, recommend, and/or implement one or moreactions for the second group of one or more assets based on adetermination that the implementation of the selected one or moreactions will lower the risk of negative impact(s) of the militarywarfare strategy, intent, tactic(s), and/or maneuver(s) of the firstgroup of one or more assets on the second group of one or more assets.4. The system of claim 1, wherein the system is configured to: determinewhether the risk of negative impact(s) of behavior(s) of the first groupof one or more assets on the second group of one or more assets isapproaching, reached, or exceeded the one or more threshold(s); andselect, recommend, and/or implement one or more actions for the secondgroup of one or more assets based on a determination that theimplementation of the selected one or more actions will lower the riskof negative impact(s) of behavior(s) of the first group of one or moreassets on the second group of one or more assets to below the one ormore threshold(s).
 5. The system of claim 1, wherein the system isconfigured to analyze the measurements/readings, the underlying rootcause(s) and motivation(s) of the behavior(s) of the at least one asset,and (a) the context(s) associated with the behavior(s) of the at leastone asset or (b) the location and the context(s) associated with thebehavior(s) of the at least one asset at the location, to therebydetermine an origination of the at least one asset, which analysisincluding the origination of the at least one asset is usable by thesystem in determining whether the at least one asset should beconsidered a threat.
 6. The system of claim 1, wherein: the at least oneasset of the first group of one or more assets comprises at least onepiece of data; and the system is configured to determine an originationof the at least one piece of data, which determination is usable by thesystem in determining whether the at least one piece of data should beconsidered a virtual threat.
 7. The system of claim 1, wherein theunderlying root cause(s) and motivation(s) of behavior(s) of the atleast one asset comprise one or more motivation(s) associated with oneor more upstream behavior(s) before the occurrence of the behavior(s) ofthe at least one asset that are directly or indirectly cause thebehavior(s) of the at least one asset and that triggered one or moreevents, actions, and/or activities that resulted in the behavior(s) ofthe at least one asset.
 8. The system of claim 1, wherein the system isconfigured to use a proof blockchain system or other distributed ledgertechnology consensus mechanism for privacy protection and security. 9.The system of claim 1, wherein the system is configured to use ablockchain system or other distributed ledger technology for dataanonymization.
 10. The system of claim 1, wherein: the system isconfigured to use one or more blockchain components including at leastone of a distributed network, a wallet, and/or a user profile; and/orthe system is configured to use artificial intelligence to observe andexamine a blockchain trail to make predictions identifying behaviorsand/or behavior patterns.
 11. The system of claim 1, wherein the systemis configured to use a proof blockchain system or other distributedledger technology consensus mechanism for storing data collected by theplurality of devices, sensors, and communication network(s), thedeterminations made by the system, the one or more threshold(s), and/orthe one or more actions to lower the risk of negative impact(s) ofbehavior(s) of the first group of one or more assets on the second groupof one or more assets.
 12. The system of claim 1, wherein the system isconfigured to be operable for determining the underlying root cause(s)and motivation(s) of behavior(s) of the at least one asset of the firstgroup of one or more assets by monitoring and assessing performance andunderlying motivation(s) associated with behavior(s) and associatedevent(s), activity(ies), and context(s) collected via a distributedgeographical computing and/or network environment(s).
 13. The system ofclaim 1, wherein the system is configured to be operable for determiningthe underlying root cause(s) and motivation(s) of behavior(s) of the atleast one asset of the first group of one or more assets by: storing aplurality of existing and/or historical events, activities, and contextrecords, wherein each existing and/or historical event, activity, andcontext record describes a corresponding event, activity, and context ina distributed computing and/or network environment, wherein a previousbaseline of motivations associated with events, activities, and contextshas been established and is stored in a baseline motivational profilefor various groups inclusive of a group(s) being analyzed; receiving, bya motivational analysis engine, an incoming event, activity, and/orcontext record describing a corresponding event, activity, and/orcontext in the distributed computing and/or network environment;performing an assessment of potential motivation(s) underlying thereceived event, activity, and/or context record by comparison to apreviously encountered similar event(s), activity(ies) and/orcontext(s); determining a degree to which the received event, activity,and/or context record is consistent with the baseline motivationalprofile for the similar event(s), activity(ies) and/or context(s), andto the extent there are any differences exceeding a certain threshold,assessing, calculating, and developing a statistical profile andassociated probabilities of the differences being caused by motivationsnot in the baseline motivational profile or in material modifications tohistorically demonstrated motivations; assessing the risk(s) ofnew/modified motivations for the event, activity, and/or context beingapplied to other event(s), activities, and/or contexts; assessing asecond group(s) of motivation(s) relative to a first group(s) ofbehavior(s) relative to the current and historical event(s), activities,and/or contexts; developing potential actions for the second group(s)that could negate or mitigate the negative effects of the new/modifiedmotivations for the event, activity, and/or context of the firstgroup(s); and developing the potential actions for an existing baselinemotivational profile of the second group(s) as well as potential actionsif the baseline motivational profile of the second group(s) is changed,the potential actions including one or more tasks to be performed,resources required, skills required, human, physical, and virtual assetsrequired, additional data monitoring and associated capabilitiesrequired.
 14. The system of claim 1, wherein: the least one asset of thefirst group of one or more assets comprises at least one person of afirst group of one or more persons; and the second group of one or moreassets comprise a second group of one or more persons that are not partof the first group of one or more persons; and/or the least one asset ofthe first group of one or more assets comprises at least one object of afirst group of one or more objects; and the second group of one or moreassets comprise a second group of one or more objects that are not partof the first group of one or more objects.
 15. The system of claim 1,wherein: the least one asset of the first group of one or more assetscomprises at least one object of a first group of one or more objects;the second group of one or more assets comprise a second group of one ormore objects that are not part of the first group of one or moreobjects; and the objects comprise one or more of military equipment,armaments, and/or personnel.
 16. The system of claim 1, wherein theassets of the first and second groups of one or more assets comprise oneor more human assets, physical assets, and/or a virtual assets.
 17. Thesystem of claim 1, wherein: the first group of one or more assetscomprise one or more of an army(ies), a military(ies), an enemy(ies), apotential enemy(ies), a sovereign nation(s) or state(s), and/or aterrorist organization(s); and the second group of one or more assetscomprise one or more of an army(ies), a military(ies), an enemy(ies), apotential enemy(ies), a sovereign nation(s) or state(s), and/or aterrorist organization(s) that are not part of the first group of one ormore assets.
 18. The system of claim 1, wherein the system is configuredto allow additions, deletions, and modifications to the one or morethresholds and to the plurality of devices, sensors, and communicationsnetwork(s) including modifying one or more of the settings, increasingor decreasing a frequency of data collection, modifying how data iscollected, and modifying type of data collected.
 19. The system of claim1, wherein the system is configured to: receive and process feedbackincluding the context(s) and/or assessment of the risk(s) associatedwith the behavior(s) of the at least one asset relative to theirimpact(s) on behavior(s) and associated motivation(s) by a second groupof one or more assets that are not part of the first group of one ormore assets; and in response to the feedback, adjust the plurality ofdevices, sensors, and communications network(s) including modifying oneor more of the settings, increasing or decreasing a frequency of datacollection, modifying how data is collected, and modifying type of datacollected.
 20. The system of claim 1, wherein: the at least one assetcomprises at least one person; and the system is configured to determinethe risk(s) associated with the behavior(s) of the at least one personrelative to their impact(s) on behavior(s) and associated motivation(s)by the second group of one or more assets relative to the one or morethresholds through analysis of information related to the underlyingroot cause(s) and motivation(s) of behavior(s) of the at least oneperson including the plurality of measurements/readings taken by theplurality of devices, sensors, and communications network(s), and (a)the context(s) associated with the behavior(s) of the at least oneperson; or (b) the location and the context(s) associated with thebehavior(s) of the at least one person; wherein the information relatedto the underlying root cause(s) and motivation(s) of behavior(s) of theat least one person includes one or more of personal physical data, bodystate data, mental state related data, environmental data, and activitydata.
 21. The system of claim 1, wherein the context(s) associated withthe behavior(s) of the at least one asset includes at least three ormore 5W1H (who, what, when, where, why, how) attributes.
 22. The systemof claim 21, wherein the context(s) associated with the behavior(s) ofthe at least one asset includes at least one environmental condition.23. The system of claim 1, wherein the context(s) associated with thebehavior(s) of the at least one asset includes: where is the location ofthe at least one asset; when the at least one asset is at the location;and an environmental condition at the location.
 24. The system of claim1, wherein the context(s) associated with the behavior(s) of the atleast one asset comprises one or more of a situation, an environmentalcondition, and a state of mind of the at least one asset.
 25. The systemof claim 1, wherein the context(s) associated with the behavior(s) ofthe at least one asset comprises: a situation and an environmentalcondition; an environmental condition and a state of mind of the atleast one asset; a situation and a state of mind of the at least oneasset; or a situation, an environmental condition, and a state of mindof the at least one asset.
 26. The system of claim 1, wherein thecontext(s) associated with the behavior(s) of the at least one assetincludes at least three or more of: where is the location of the atleast one asset; why the at least one asset is at the location; who (ifanyone) is virtually and/or actually with the at least one asset at thelocation or nearby the location within audible, visual, and/orelectronic detection range of the devices, sensors, and/or communicationnetwork(s); what the at least one asset is doing at the location; whenthe at least one asset is at the location; how the at least one assetarrived at the location and/or how will the at least one asset leave thelocation; and an environmental condition at the location.
 27. The systemof claim 1, wherein the context(s) associated with the behavior(s) ofthe at least one asset includes at least three or more of: where is theat least one asset; why is the at least one asset exhibiting thebehavior(s); who (if anyone) is virtually and/or actually with the atleast one asset or nearby the at least one asset within audible, visual,and/or electronic detection range of the devices, sensors, and/orcommunication network(s); what is the at least one asset doing; when isthe at least one asset exhibiting the behavior(s); how is the at leastone asset exhibiting the behavior(s); and an environmental condition.28. The system of claim 1, wherein the plurality of devices, sensors,and communication network(s) comprises at least two different types ofdevices, sensors, and communication network(s) configured to take aplurality of different types of measurements/readings.
 29. The system ofclaim 1, wherein the system is configured to determine the context(s)associated with the behavior(s) of the at least one asset including whythe at least one asset is exhibiting the behavior(s) based on at leastone or more of: where is the at least one asset; who (if anyone) isvirtually and/or actually with the at least one asset or nearby the atleast one asset within audible, visual, and/or electronic detectionrange of the devices, sensors, and/or communication network(s); what isthe at least one asset doing; when is the at least one asset exhibitingthe behavior(s); how is the at least one asset exhibiting thebehavior(s); and at least one environmental condition.
 30. The system ofclaim 1, wherein the system is configured to monitor result(s) of theone or more actions to thereby determine effectiveness of risk loweringaction(s) and determine modification(s), if any, to profileparameter(s), risk determination algorithm(s), and/ormeasurements/readings taken by the plurality of devices, sensors, andcommunications network(s).
 31. The system of claim 1, wherein: the atleast one asset comprises at least one human asset, and the behavior(s)of at least one human asset comprises a human behavior; and/or the atleast one asset comprises at least one physical asset, and thebehavior(s) of at least one physical asset comprises a physical activityincluding movement or relocation of the physical asset; and/or the atleast one asset comprises at least one virtual asset, and thebehavior(s) of at least one virtual asset comprises a virtual activity.32. A system comprising a plurality of devices, sensors, andcommunication network(s), the system configured to determine, through aplurality of measurements/readings taken by the plurality of devices,sensors, and communication network(s), behavior(s) of at least one assetand (a) context(s) associated with the behavior(s) of the at least oneasset; or (b) location and the context(s) associated with thebehavior(s) of the at least one asset; wherein the context(s) associatedwith the behavior(s) of the at least one asset includes at least threeor more 5W1H (who, what, when, where, why, how) attributes; and/orwherein the system is configured to determine the context(s) associatedwith the behavior(s) of the at least one asset including why the atleast one asset is exhibiting the behavior(s) based on at least one ormore of: where is the at least one asset; who (if anyone) is virtuallyand/or actually with the at least one asset or nearby the at least oneasset within audible, visual, and/or electronic detection range of thedevices, sensors, and/or communication network(s); what is the at leastone asset doing; when is the at least one asset exhibiting thebehavior(s); how is the at least one asset exhibiting the behavior(s);and at least one environmental condition.
 33. The system of claim 32,wherein the context(s) associated with the behavior(s) of the at leastone asset includes at least three or more of: where is the at least oneasset; why is the at least one asset exhibiting the behavior(s); who (ifanyone) is virtually and/or actually with the at least one asset ornearby the at least one asset within audible, visual, and/or electronicdetection range of the devices, sensors, and/or communicationnetwork(s); what is the at least one asset doing; when is the at leastone asset exhibiting the behavior(s); how is the at least one assetexhibiting the behavior(s); and an environmental condition.
 34. Thesystem of claim 32, wherein the context(s) associated with thebehavior(s) of the at least one asset includes: where is the location ofthe at least one asset; when the at least one asset is at the location;and an environmental condition at the location.
 35. The system of claim32, wherein the system is configured to determine, through the pluralityof measurements/readings taken by the plurality of devices, underlyingroot cause(s) and motivation(s) of the behavior(s) of the at least oneasset.
 36. The system of claim 35, wherein the system is configured toanalyze the measurements/readings, the underlying root cause(s) andmotivation(s) of the behavior(s) of the at least one asset, and (a) thecontext(s) associated with the behavior(s) of the at least one asset or(b) the location and the context(s) associated with the behavior(s) ofthe at least one asset at the location, to thereby determine risk(s)associated with the behavior(s) of the at least one asset relative toimpact(s) on behavior(s) and associated motivation(s) by at least oneother asset.
 37. The system of claim 36, wherein the system isconfigured to facilitate one or more actions to lower the risk ofnegative impact(s) of the behavior(s) of the at least one asset on theat least one other asset when compared to one or more threshold(s). 38.The system of claim 37, wherein the system is configured to monitorresult(s) of the one or more actions to thereby determine effectivenessof risk lowering action(s) and determine modification(s), if any, toprofile parameter(s), risk determination algorithm(s), and/ormeasurements/readings taken by the plurality of devices, sensors, andcommunications network(s).
 39. The system of claim 32, wherein thesystem is configured to analyze the measurements/readings, thebehavior(s) of the at least one asset, and (a) the context(s) associatedwith the behavior(s) of the at least one asset or (b) the location andthe context(s) associated with the behavior(s) of the at least one assetat the location, to thereby determine a risk of a second behavior(s) ofthe at least one asset relative to a trigger threshold(s).
 40. Thesystem of claim 39, wherein the system is configured to facilitate oneor more actions to lower the risk of the second behavior(s) associatedwith a trigger(s) from reaching or exceeding the trigger threshold(s)associated with the second behavior(s) before the second behavioroccurs.
 41. The system of claim 40, wherein the system is configured tomonitor result(s) of the one or more actions to thereby determineeffectiveness of risk lowering action(s) and determine modification(s),if any, to profile parameter(s) of the at least one asset, riskdetermination algorithm(s) and/or measurements/readings taken by theplurality of devices, sensors, and communications network(s) relative tothe trigger(s) and/or the second behavior(s) of the at least one asset.42. The system of claim 32, wherein the at least one asset comprises oneor more of a human asset, a physical asset, and/or a virtual asset. 43.A computer-implemented method for monitoring and assessing performanceand underlying motivation(s) associated with behaviors and associatedevents, activities, and contexts collected via a distributedgeographical computing, and/or network environment(s), the methodcomprising: storing a plurality of existing and/or historical events,activities, and context records, wherein each existing and/or historicalevent, activity, and context record describes a corresponding event,activity, and context in a distributed computing and/or networkenvironment, wherein a previous baseline of motivations associated withevents, activities, and contexts has been established and is stored in abaseline motivational profile for various groups inclusive of a group(s)being analyzed; receiving, by a motivational analysis engine of asystem, an incoming event, activity, and/or context record describing acorresponding event, activity, and/or context in the distributedcomputing and/or network environment; performing an assessment ofpotential motivation(s) underlying the received event, activity, and/orcontext record by comparison to a previously encountered similarevent(s), activity(ies) and/or context(s); determining a degree to whichthe received event, activity, and/or context record is consistent withthe baseline motivational profile for the similar event(s),activity(ies) and/or context(s), and to the extent there are anydifferences exceeding a certain threshold, assessing, calculating, anddeveloping a statistical profile and associated probabilities of thedifferences being caused by motivations not in the baseline motivationalprofile or in material modifications to historically demonstratedmotivations; assessing the risk(s) of new/modified motivations for theevent, activity, and/or context being applied to other event(s),activities, and/or contexts; assessing a second group(s) ofmotivation(s) relative to a first group(s) of behavior(s) relative tothe current and historical event(s), activities, and/or contexts;developing potential actions for the second group(s) that could negateor mitigate the negative effects of the new/modified motivations for theevent, activity, and/or context of the first group(s); and developingthe potential actions for an existing baseline motivational profile ofthe second group(s) as well as potential actions if the baselinemotivational profile of the second group(s) is changed, the potentialactions including one or more tasks to be performed, resources required,skills required, human, physical, and virtual assets required,additional data monitoring and associated capabilities required.
 44. Thecomputer-implemented method of claim 43, wherein the method includesassessing, via the motivational analysis engine, inputs into and/oroutputs from the system including evaluation of one or more incomingrequests, external requests, actions from the system, and actions fromothers, which evaluation may include potential tasking and assessment ofoverall morality of actions and/or ethics of actions.
 45. Thecomputer-implemented method of claim 43, wherein the method includesallowing user intervention to prevent and/or address prohibited,wrongful, immoral, and/or unethical action(s) of the system andproviding machine learning to inhibit the system from repeat performanceof the prohibited and/or wrongful action(s).
 46. Thecomputer-implemented method of claim 43, wherein the method includes:determining, via a reputation engine connected with the system, whethera user is untrustworthy based one or more wrongful, disreputable,immoral, and/or unethical user actions and/or requests by the user; andif the user is determined to be untrustworthy, reducing userprivilege(s) to the system and/or locking the user out from system.